enterprise ai automation agency - UK
Enterprise AI platform development in London
Make your AI an asset, not a dependency.
Steer73 builds enterprise AI platforms you fully own, on Microsoft Azure and .NET, enabling you to roll out AI across your business faster and more cost-effectively than SaaS or traditional bespoke development.
Founded
2014
Failed projects
0
Countries delivered in
10+
IP ownership – yours, always
100%
We plan, build, launch and manage enterprise AI platforms and systems for
A leading enterprise AI platform partner in London
We help organisations build enterprise AI platforms they own, platforms that scale across the business, adapt as AI evolves, and deliver measurable ROI without the cost, lock-in, or risk of failure typically associated with custom AI development or SaaS rollouts.
With a decade of building production-grade systems on Microsoft Azure and .NET, our full in-house team (architects, AI engineers, data specialists, UX designers, product managers, and QA) delivers across every stage of an enterprise AI platform project. From mid-market businesses to global enterprises in regulated sectors, we have the expertise to design platforms that match the way your organisation actually works, rather than forcing generic SaaS workflows.
More than 10 years of successful delivery across 10+ countries has taught us where enterprise AI projects fail, and how to avoid it. Every system we have built has delivered measurable improvements, such as removed manual effort, reduced operational cost, or unlocked new business capability.
We build platforms designed to measurable return on investment, not just to demonstrate technology.
In over 10 years we have never failed to deliver a project. We can contractually guarantee yours will not be the first.
We build long-term client partnerships, with many of the systems we built years ago now in their ninth and tenth year of operation.
Our enterprise AI platform services.
Need help with your enterprise AI platform project?
Enterprise AI platform Discovery
Most enterprise AI projects fail because the wrong thing gets built.
We start with a free Discovery service comprised of structured workshops to understand your business case, your technical environment, and the AI use cases that will actually deliver ROI.
We don’t need detailed specs or requirements, and can work from a single sentence description of your situation if that is all you have.
Outcome: A clear, prioritised plan for your enterprise AI platform, before a line of code is written or any commitment is made.
Enterprise AI platform design and architecture
Your platform is the foundation everything else runs on.
We design the core architecture (data ingestion, AI orchestration, business logic, security, integrations) on Microsoft Azure and .NET. Built once, then used across every department. Modular by design so you can swap AI services and models as the landscape evolves.
Outcome: A platform that supports unlimited use cases without rebuilding the foundations every time.
An AI platform is usually only useful when it runs your business processes. We build the workflows that automate finance, sales, HR, legal, operations, and customer-facing processes, tailored to how your business actually works, not how a SaaS tool wants you to work.
Outcome: End-to-end process automation that fits your business, not the other way around.
AI use case design and deployment
From invoice matching to contract redlining, CV screening to quoting agents, each AI use case is designed around your specific processes, data, and decision rules. Deploy one to prove ROI, then deploy more on the same foundation.
Outcome: Faster, cheaper rollout of every subsequent AI capability across your business, because the platform is already there.
AI integration services
Your platform will likely need to connect to other systems such as your ERP, data warehouse, legacy systems, line-of-business applications, document stores, and external data sources.
We design and build the integrations that make AI useful.
Outcome: AI embedded into your operations, not bolted on alongside them.
Enterprise AI portals and admin interfaces
Internal teams often need clear interfaces to manage AI workflows, review outputs, and intervene when judgement is required. External users may need portal access to the same underlying platform.
We design and build the UI layer that turns the platform into something your teams actually want to use.
Outcome: High adoption and a better employee, customer, and supplier experience than the typical SaaS sprawl.
Long-term enterprise AI platform management
AI is moving fast and platforms need to evolve with it.
Our Continuity and Managed Service models keep your platform current, with model upgrades, security patches, new AI capabilities, and ongoing optimisation. You retain full ownership at every stage.
Outcome: A platform that gets better over time without locking you into a single supplier.
Common enterprise AI use cases
What an enterprise AI platform delivers depends entirely on the use cases you deploy on it. Below is a non-exhaustive list of the common workflows and automations that we can build, grouped by business function. Each is a candidate use case for your platform, and each becomes faster and cheaper to deploy because the foundations are already in place.
Finance
Finance is where many clients start. The ROI is often the most immediate and quantifiable, and document-heavy reconciliation work ties up time that should sit in higher-value analysis.
- Accounts payable automation: Invoice matching against purchase orders and delivery notes
- Data extraction from PDFs, emails, and supplier statements
- Supplier statement reconciliation
- Expense claim validation and categorisation
- Bank transaction categorisation and payment matching
- Overdue invoice chasing and follow-up
Operations
Operational work involves constant data movement between systems, exception handling, and quality checking. Automating the routine parts removes friction without removing the human judgment needed for the difficult cases.
- Manual data entry and synchronisation between systems
- Order processing and validation
- Scheduling of jobs, routes, or resources
- Exception handling where data does not match
- SOP compliance checks
- Quality assurance spot checks and customer status updates
Sales
Sales teams spend significant time on activities that AI can accelerate or automate entirely, freeing the team for the conversations that actually convert.
- Quoting agents that assemble accurate quotes from product catalogues
- Tender and RFP scanning, qualification, and response drafting
- Government gateway and opportunity searching
- Prospect list cleaning and enrichment
- Pipeline management and CRM hygiene
Legal and compliance
Legal and compliance functions are some of the most document-heavy in any business. AI accelerates review work substantially while preserving audit trails.
- Contract redlining and clause review
- NDA review and approval routing
- Contract version comparison
- Compliance checks against internal policies
- Legal inbox triage and routing
HR
HR teams handle a constant flow of repetitive workflows around hiring, onboarding, and internal queries. AI can handle the repetitive parts while keeping humans in the loop for the decisions that matter.
- CV screening and shortlisting
- Interview scheduling and coordination
- Onboarding paperwork and training tracking
- Policy acknowledgement tracking
- Answering repetitive HR questions on leave, benefits, and policies
Customer support
Customer support benefits from AI in two distinct ways: as customer-facing agents that handle volume directly, and as internal copilots that help human agents respond faster and more accurately.
- Support agents trained on your internal knowledge bases
- Support copilots helping human agents access answers faster
- Automated ticket routing and prioritisation
- First-response drafting for human review
Marketing
Marketing teams often run a high cadence of content and reporting requirements against limited resource. AI handles the repetitive production and analysis work so the team can focus on strategy and creative.
- Content repurposing across formats (blog to LinkedIn to email)
- Campaign performance reporting and summaries
- Creative production support
- Content creation against brand guidelines
Example enterprise AI platform projects
With a proven track record of delivering production-grade AI and enterprise platform projects for global organisations and mid-sized businesses, we have the experience to design enterprise AI platforms that integrate deeply with operational systems and deliver measurable business outcomes.
CASE 01
MANUFACTURING, MOBILE, AI, ERP, GLOBAL ROLLOUT
Bringing real-time AI pump diagnostics to KSB, a global manufacturer.
KSB, one of the world’s leading manufacturers of industrial pumps and valves, with customers including BrewDog, Continental, Nestlé and Miele, needed to bring their proprietary pump diagnostics algorithm directly into their customers’ hands.
Steer73 designed and built an Industry 4.0 mobile app that captures and analyses pump audio recordings on-device in 20 seconds, identifies pumps via QR scan, routes support enquiries to geographically appropriate engineers, and pipes all recordings back into KSB’s ERP to continuously improve the underlying algorithm.
Pump performance is now measurable from a smartphone in seconds, and KSB has built a new layer of operational and commercial intelligence into its ERP, exactly the kind of AI-and-integration engineering that underpins our platform approach today.
CASE 02
FIRE SAFETY & SECURITY, CUSTOMER PORTAL, MULTI-REGION, MULTILINGUAL
A global enterprise platform unifying customer data for Chubb Fire & Security.
Chubb Fire & Security, a British multinational providing fire protection and security systems to businesses worldwide with more than 200 years of heritage, needed to break down information silos across its global operations. Two large, separate data sources sat at the heart of the problem: an enormous maintenance database holding ERP data, invoices, and orders for safety and security equipment across customer sites, and a monitoring data source covering alarm events and keyholder configurations.
Steer73 designed and built a global customer portal platform with intelligent data ingestion pipelines that combine both sources into one operational view. A single codebase supports multiple regional deployments, with multilingual support, centralised admin, and a scalable architecture designed to grow as Chubb adds further markets.
Customers globally now have unified portal access, and Chubb’s operations teams have consolidated visibility for the first time, built on the same Microsoft Azure foundations that underpin our enterprise AI platform work.
A global enterprise platform unifying customer data for Chubb Fire & Security.
Chubb Fire & Security, a British multinational providing fire protection and security systems to businesses worldwide with more than 200 years of heritage, needed to break down information silos across its global operations. Two large, separate data sources sat at the heart of the problem: an enormous maintenance database holding ERP data, invoices, and orders for safety and security equipment across customer sites, and a monitoring data source covering alarm events and keyholder configurations.
Steer73 designed and built a global customer portal platform with intelligent data ingestion pipelines that combine both sources into one operational view. A single codebase supports multiple regional deployments, with multilingual support, centralised admin, and a scalable architecture designed to grow as Chubb adds further markets.
Customers globally now have unified portal access, and Chubb’s operations teams have consolidated visibility for the first time, built on the same Microsoft Azure foundations that underpin our enterprise AI platform work.
A global enterprise platform unifying customer data for Chubb Fire & Security.
Chubb Fire & Security, a British multinational providing fire protection and security systems to businesses worldwide with more than 200 years of heritage, needed to break down information silos across its global operations. Two large, separate data sources sat at the heart of the problem: an enormous maintenance database holding ERP data, invoices, and orders for safety and security equipment across customer sites, and a monitoring data source covering alarm events and keyholder configurations.
Steer73 designed and built a global customer portal platform with intelligent data ingestion pipelines that combine both sources into one operational view. A single codebase supports multiple regional deployments, with multilingual support, centralised admin, and a scalable architecture designed to grow as Chubb adds further markets.
Customers globally now have unified portal access, and Chubb’s operations teams have consolidated visibility for the first time, built on the same Microsoft Azure foundations that underpin our enterprise AI platform work.
Why choose Steer73 for your enterprise AI platform?
Guaranteed success
In over 10 years of building enterprise systems, across 10+ countries for some of the world’s largest companies, we have never failed to deliver a project. We can contractually guarantee yours will not be the first.
The combination of our Digital Transformation Platform, our zero-juniors hiring model, and our rigorous Discovery and Scoping methodology removes the failure modes that derail many enterprise AI initiatives.
Proven processes
Our delivery methodology has been refined across more than 10 years of enterprise projects.
We translate ambiguous business problems into precise technical solutions through a structured five-phase process, from Discovery through to launch and long-term management.
Every phase removes ambiguity before the next begins, which is why we can offer fixed-price enterprise AI platform projects when most agencies cannot.
Maximising ROI
An enterprise AI platform should be an investment, not a cost centre.
We design platforms that solve foundational problems once (data integration, AI orchestration, security, governance) and then unlock use case after use case across the business. Each new AI application costs less than the last because the platform is already there. Your platform compounds in value over time, rather than draining it through licence fees.
London based, global team
Steer73 is headquartered in London with senior team members available for on-site workshops and reviews. Our wider team spans more than seven countries, which lets us hire the best AI and platform talent regardless of location while keeping commercial accountability local. The result is the depth of a global consultancy with the responsiveness of a local partner.
Faster, higher ROI solutions
Our Digital Transformation Platform and our Cambrian Operating System, which comprises a decade of pre-built components, frameworks, and proven patterns, means we deliver enterprise AI platforms at speeds closer to SaaS configuration than to traditional bespoke development. Custom precision, off-the-shelf pace. Your first AI use case can be in production in months, not years, with every subsequent use case faster and cheaper than the last
Meet us in London or give us a call
London office: Steer73 – Enterprise AI platform partner in London
167-169 Great Portland St (5th Floor), London, W1W 5PF, United Kingdom
Phone number: +44 2038 550 237
Email: hello@steer73.com
Comprehensive AI automation services
From initial AI opportunity assessment through to long-term platform management, our London-based team delivers the full range of enterprise AI platform capabilities, combining strategic, technical, and operational expertise on a single Microsoft Azure and .NET foundation.
AI and automation strategy and roadmapping
Identify where AI will deliver the highest ROI across your business, prioritise use cases against business goals, and produce a phased roadmap aligned with your operational realities and budget.
Enterprise data architecture and integration
Design and build the data foundations your AI platform needs: ingestion pipelines, structured and unstructured data handling, integration with ERPs, data warehouses, and line-of-business systems.
AI model selection, orchestration and lifecycle management
A flexible AI layer designed to evolve as the market evolves. Swap models (OpenAI, Azure AI, Anthropic, open source) without rebuilding the workflows that depend on them.
Document processing and intelligent extraction
AI-driven extraction of structured data from PDFs, contracts, invoices, emails, and other documents. Built using enterprise tools such as Azure AI Document Intelligence.
Business process automation and workflow orchestration
Automate end-to-end processes across finance, sales, HR, legal, operations, and customer-facing functions. AI services, traditional automation, and human-in-the-loop combine where each is the right answer.
Operational admin portal development
Internal interfaces that give your teams control over AI workflows: review outputs, manage exceptions, oversee performance, and intervene when judgement is needed. Built for adoption.
Customer and supplier portal development
Extend the same enterprise AI platform to external users, customers, suppliers, sub-contractors, through tailored portals that integrate cleanly with the underlying workflows and identity systems.
AI governance, security, and compliance
Security, access controls, audit logging, and human-in-the-loop oversight built into the platform. Experience designing for regulated industries where AI cannot be a black box.
Our unique approach to enterprise AI platforms
Every enterprise AI platform project is different, so we tailor our approach to your business, your existing systems, and your internal capability. With a full in-house team of architects, AI engineers, data specialists, designers, QA, and product managers, we deliver end-to-end, from strategy through to long-term platform management.
PHASE 01 — DISCOVERY
Strategy-led discovery
We start with the business case, not the technology. Through a free Discovery project, we map your operational reality, identify where AI will deliver measurable ROI, and prioritise use cases against your strategic goals. Our Digital Transformation Platform, a decade of refined frameworks, exercises, and analytical tools, ensures we surface the right requirements quickly and comprehensively.
You leave Discovery with a clear, prioritised plan and confidence that the platform you invest in will solve the right problems. This is where most agencies skip ahead. We don’t.
PHASE 02 — SCOPING
Choosing the right AI architecture and models
AI is moving faster than any platform decision should depend on. We design platforms with a flexible AI service layer: your workflows, your business logic, your data sit above the model layer. Swap OpenAI for Anthropic, or Azure AI for a specialised provider, without rebuilding what runs on top.
We work closely with your team to choose the right architectural patterns for each use case, agentic where appropriate, traditional automation where it’s faster and more reliable, human-in-the-loop where the stakes demand it. The platform is built around your processes, not generic SaaS workflows.
Fast, secure platform development with full transparency
Our CambrianOS AI & Automation platform enables us to deliver enterprise AI platforms at speeds closer to SaaS configuration than to traditional bespoke development. Pre-built components, proven patterns, and Microsoft Azure foundations remove the foundational work most projects rebuild from scratch.
You have full visibility throughout: weekly progress reviews, clear backlogs, transparent risk and dependency management. Workshops happen at our London office, on-site at your location, or remotely as suits your team. Security, governance, and access controls are core to the requirements we define with you, ensuring the optimum balance between security, cost, and speed.
PHASE 04 — BUILD & SAT
A partner for whatever comes next
Your enterprise AI platform is the foundation for years of business improvement. We design it to evolve for new use cases, new business areas, new models, without architectural rebuilds. Our Managed Service model gives you a dedicated long-term team that knows your platform intimately and continues to ship improvements quarter after quarter.
You retain full ownership at every stage. If you decide to take the work in-house, we hand over a fully documented platform with training. The only tie-in to us is our quality.
Phase 01 — Discovery
Strategy-led discovery
We start with the business case, not the technology. Through a free Discovery project, we map your operational reality, identify where AI will deliver measurable ROI, and prioritise use cases against your strategic goals. Our Digital Transformation Platform, a decade of refined frameworks, exercises, and analytical tools, ensures we surface the right requirements quickly and comprehensively.
You leave Discovery with a clear, prioritised plan and confidence that the platform you invest in will solve the right problems. This is where most agencies skip ahead. We don’t.
Phase 02 — Scoping
Choosing the right AI architecture and models
AI is moving faster than any platform decision should depend on. We design platforms with a flexible AI service layer: your workflows, your business logic, your data sit above the model layer. Swap OpenAI for Anthropic, or Azure AI for a specialised provider, without rebuilding what runs on top.
We work closely with your team to choose the right architectural patterns for each use case, agentic where appropriate, traditional automation where it’s faster and more reliable, human-in-the-loop where the stakes demand it. The platform is built around your processes, not generic SaaS workflows.
Phase 03 — Solution definition
Fast, secure platform development with full transparency
Our CambrianOS AI & Automation platform enables us to deliver enterprise AI platforms at speeds closer to SaaS configuration than to traditional bespoke development. Pre-built components, proven patterns, and Microsoft Azure foundations remove the foundational work most projects rebuild from scratch.
You have full visibility throughout: weekly progress reviews, clear backlogs, transparent risk and dependency management. Workshops happen at our London office, on-site at your location, or remotely as suits your team. Security, governance, and access controls are core to the requirements we define with you, ensuring the optimum balance between security, cost, and speed.
Phase 04 — Build & SAT
A partner for whatever comes next
Your enterprise AI platform is the foundation for years of business improvement. We design it to evolve for new use cases, new business areas, new models, without architectural rebuilds. Our Managed Service model gives you a dedicated long-term team that knows your platform intimately and continues to ship improvements quarter after quarter.
You retain full ownership at every stage. If you decide to take the work in-house, we hand over a fully documented platform with training. The only tie-in to us is our quality.
Phase 05 — UAT & launch
Validate, train, deploy
Final acceptance testing with your team, training and onboarding, then launch. Not handed over and forgotten, set up for ongoing success.
Enterprise AI platform vs SaaS, RPA, Copilot, and in-house build.
Many enterprise AI buyers are comparing four options: mass-market AI tools, point-solution SaaS, RPA platforms, and building in-house. Each has a role. None of them, on their own, delivers what an owned enterprise AI platform delivers. Below is the honest comparison, based on what we see when we work with clients who arrived having already tried one or more of these routes.
Microsoft Copilot, ChatGPT Enterprise, and other mass-market AI tools
Mass-market tools like Copilot and ChatGPT are useful for individual productivity but they unlock only a fraction of the value AI can deliver across an operational business. They do not automate end-to-end processes, they do not integrate with your operational systems, and they offer generic workflows your team has to work around. An enterprise AI platform sits at the other end of the spectrum: AI embedded into your processes, your data, your workflows. Most of our clients run both, mass-market tools for productivity, and an owned platform for operations.
SaaS AI point solutions
SaaS AI tools solve narrow problems. They are easy to start with and hard to live with at scale: vendor lock-in, ever-rising licence fees, generic workflows your team adapts around, and data restrictions that limit what you can do with your own information. Each tool solves one slice of one problem, so you end up with a fragmented web of subscriptions and no shared foundation. With an owned enterprise AI platform, foundations are built once and reused across every use case, your costs fall over time rather than rise.
RPA platforms (UiPath, Power Automate, Blue Prism)
RPA tools are strong for rule-based, repetitive tasks but weaker for the AI-driven judgement modern business processes require. They also tend to proliferate, each automation becomes a separate artefact with its own maintenance burden. An enterprise AI platform combines AI services, traditional automation, and human oversight on one consistent foundation. Where RPA is the right answer, the platform uses it. Where AI judgement is required, the platform uses that. The same security model, governance, and operational visibility are present across every workflow.
Building in-house with hired AI engineers
Building in-house can work, if you have time, capital, and the appetite for the 95% failure rate MIT has documented for enterprise AI projects. Most organisations don’t. Steer73’s model is different: we build the platform with you, hand it over fully documented if you ever want to take it in-house, and stay engaged for as long as you find it useful. You get an enterprise AI platform without absorbing the risk of building one from scratch.
Our role is not to push one model over another. It is to help you make the right call honestly.
Many of our clients use mass-market tools and an owned platform in parallel. They solve different problems.
Flexible pricing & engagement models
Enterprise AI platform projects vary in scope, governance requirements, and pace. We offer three engagement models so you can align delivery with your internal processes, budget controls, and risk appetite.
Fixed price model
Fixed price, fixed scope. Best for clearly-defined initial platform builds where you need budget certainty before development begins. Suitable when governance and predictability are paramount, for example when the project needs board approval against a hard number. Any scope variations are handled through a formal change-request process so cost remains controlled throughout.
Time & materials
Fixed budget, adjustable scope. Best when requirements are likely to evolve during delivery, which is often the case with enterprise AI platform projects as new use cases come into view. Weekly releases with continuous feedback. Full cost transparency throughout. Scope and priorities can adjust as you learn what the platform should do.
Managed service model
Long-term development partnership. A dedicated team works as your extended product development capability, delivering improvements, maintenance, and new use cases as the business evolves. Lower day rates than project engagements. The most common model for enterprise AI platform clients beyond the initial build, because the platform keeps producing new value year after year.
Technologies we use for enterprise AI platforms
Microsoft-first by default. Microsoft Azure and .NET are the right foundation for most enterprise AI platform projects: best fit for enterprise IT strategies, lowest operational overhead, broadest ecosystem, strong long-term support.
We are technology-agnostic where there is a better answer for your specific outcome, and we recommend the right stack honestly.
Platform foundation
The .NET and Microsoft Azure foundation that everything else runs on. A decade of production-grade enterprise systems built on this stack.
- .NET
- Microsoft Azure (App Service, Functions, SQL, Storage, Key Vault)
- ABP.io rapid development framework
AI services and models
A flexible AI layer that can swap models and providers as the market evolves. Your workflows and business logic do not depend on any single vendor.
- Azure OpenAI
- Azure AI Document Intelligence
- Azure AI Services (cognitive search, vision, language)
- Microsoft 365 Copilot integration
- OpenAI, Claude, and other models via the orchestration layer
Data and integration
The data pipelines and integration layer that connect your enterprise AI platform to the rest of your business.
- Azure Data Factory
- Azure API Management
- Microsoft Power Platform (Logic Apps, Power Automate)
- Microsoft Dynamics 365 and other ERP integrations
- Microsoft Power BI
- Microsoft SharePoint
Security and identity
Enterprise-grade security and identity management.
- Microsoft Entra (identity and access management)
- Azure Key Vault
- Microsoft DevOps secure pipelines
- Azure Application Insights (audit and monitoring)
Frontend, portals, and mobile
Modern, accessible interfaces for the internal teams and external users of the platform.
- React
- Blazor
- iOS and Android (native and cross-platform)
- Steer Continuity Support Services
Built to evolve as the AI landscape evolves
In a volatile AI market, the biggest risk is locking your business into a tool that cannot move with it. Steer73’s architectural answer is built into the solution: a modular AI service layer, decoupled business logic, long-term platform partnership, and full ownership of the roadmap. Your platform is designed to outlast the current generation of AI tooling.We move through a structured, iterative process that turns your idea into a working product, refining along the way to ensure the right blend of features, usability, scalability and ROI.
Modular AI architecture
Models change. Vendors change. Pricing changes. The right architectural answer is to keep your business logic, workflows, and data separate from the AI service layer. We build platforms with a flexible AI orchestration layer that can swap models (OpenAI to Anthropic, Azure OpenAI to a specialised provider) without rebuilding what runs on top. When the next generation of models lands, you adopt it. You don’t rebuild for it.
Your business logic, not your model's logic
Workflows, decision rules, and process automation are owned by you and live in your platform. They survive model changes, vendor changes, and architectural evolution. SaaS tools do not offer this: the workflow you build inside a SaaS vendor is locked to that vendor’s roadmap. With an owned platform, your operational logic is an asset that compounds in value over time.
Long-term platform management
Continuity Services and Managed Service teams keep your platform current as Azure adds capabilities, AI models improve, security requirements evolve, and your business needs change. New AI capabilities are evaluated for fit and integrated when they make business sense, not when a vendor announces them. The platform stays modern without surprise migrations or breaking changes.
You own the roadmap
There is no SaaS vendor deciding the platform’s direction for you. Features that matter to your business get prioritised. Features that don’t, don’t. New AI capabilities are adopted when they deliver measurable ROI, not when an external roadmap dictates. This is the freedom an owned enterprise AI platform delivers, and the freedom no SaaS tool ever will.
“The team at Steer73 are extremely well versed in their fields and regularly provide informative updates on technology and external industry trends, which we can translate to benefit our customers and anticipate challenges.”
“Steer73 have offered us a high-quality service from the beginning of the project, working with us to analyse where our current range of digital products were successful and where they were failing, providing both the tools and the encouragement to grow our business.”
“It’s been an absolute pleasure to work with Steer73, very professional, very fast and they keep us in check – keep us focused on answering the important issues at hand.”
Chubb
What our clients say about us
How much does an enterprise AI platform cost?
The honest answer: it depends. Cost varies based on the scope of the initial deployment, the number and complexity of integrations, the AI use cases being deployed, and your ongoing management model. There is no fixed price list, because no two enterprise AI platforms are the same. What we can tell you is that the initial platform foundation plus first business area is the typical starting investment, and that our free Discovery service will give you a real estimate before any commitment.
What affects the cost?
Scope of initial deployment
How many use cases are included in version 1. Most clients start with one business area (finance, sales, HR) and add more as the platform proves ROI.
Number and complexity of integrations
Connecting to one ERP is straightforward. Connecting to a fragmented legacy estate with multiple data sources, line-of-business systems, and external services takes longer and costs more, but is often where the highest ROI lies.
AI services and model choice
Different AI services and models carry different commercial and technical implications. We help you make the right choice for each use case based on accuracy, cost, latency, and data residency requirements.
Security, governance, and compliance requirements
Regulated industries (finance, healthcare, legal) carry additional security, audit, and compliance overhead. We can build for these requirements from the start rather than retrofitting them later.
Volume, scale, and performance requirements
High-volume, real-time workloads have different architectural and infrastructure implications than lower-volume internal processes. We design the platform for the load it actually needs to handle, not the load a vendor wants to sell you.
Ongoing management model
Continuity Services, Managed Service, or hand-over to in-house teams all have different ongoing cost profiles. The right model depends on your internal capability and the rate at which you want to add new use cases.
For a real number tailored to your specific situation, our free Discovery service is the starting point.
1. Initial consultation
A short, no-pressure call to discuss your business goals and where AI could deliver value. We will give you early cost guidance and a clear sense of whether an enterprise AI platform is the right answer for your situation.
2. Free discovery project
A structured deep dive into your problem, your operational reality, your technical environment, and your priorities. You leave Discovery with a clear plan, a prioritised set of use cases, and a real estimate for the work.
3. Clear cost plan and final decision
With the Discovery output, we align budget, scope, technology choices, and delivery model to maximise ROI. You have everything you need to make an informed final decision, with no obligation to proceed.
Not sure where to start with your enterprise AI platform
We make it easy to get clarity without obligation.
1. Initial consultation
A short, no-pressure call to discuss your business goals and where AI could deliver value. We will give you early cost guidance and a clear sense of whether an enterprise AI platform is the right answer for your situation.
2. Free discovery project
A structured deep dive into your problem, your operational reality, your technical environment, and your priorities. You leave Discovery with a clear plan, a prioritised set of use cases, and a real estimate for the work.
3. Clear cost plan and final decision
With the Discovery output, we align budget, scope, technology choices, and delivery model to maximise ROI. You have everything you need to make an informed final decision, with no obligation to proceed.
Our enterprise AI platform articles
Custom software is cheaper than SaaS
Why the total cost of ownership of an owned platform almost always beats SaaS over a five-year horizon.
Automating PDF data extraction with AI and Azure
A technical look at how Azure AI Document Intelligence and orchestration patterns make AI-powered document automation production-ready.
Your AI is not as smart as it could be
Why most enterprise AI initiatives underperform, and what to do about it.
Seven key strategies for data migration
Migrating data in a usable and cost effective way is critical to successful system modernisation and migration. We explore seven general strategies that can be applied to your data migration project.
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Discover how successful digital products emerge at the intersection of business needs, user expectations, and technological capabilities. Our proven methodology that has delivered consistent success across complex projects for over 8 years.
FAQs, enterprise AI platforms, London
The initial platform foundation plus first business area is typically delivered in two to six months. Each subsequent use case is faster, sometimes weeks rather than months, because the platform foundations are already in place. Timelines depend on integration complexity, AI use case complexity, and the rate at which your team can absorb new tooling. Our free Discovery service will give you a real estimate based on your situation.
Costs vary widely based on scope, integrations, AI use case complexity, and security requirements. There is no fixed price, every enterprise AI platform is built around the specific business it serves. What we can offer is a free Discovery service that produces a real estimate before you have committed to anything. Beyond the initial build, ongoing platform management costs are typically a fraction of the SaaS alternative over a multi-year horizon.
Copilot and ChatGPT are productivity tools, useful for individual users but limited to generic workflows and the data they can access. An enterprise AI platform sits at a different level: it embeds AI into your operational processes, integrates with your ERPs and line-of-business systems, automates end-to-end workflows, and runs across the whole business. Most of our clients use both, mass-market tools for individuals, and an owned platform for operations.
RPA tools are excellent for rule-based, repetitive tasks. They are weaker for AI-driven judgement and tend to proliferate, each automation becomes its own artefact with its own maintenance burden. An enterprise AI platform combines RPA, AI services, and human oversight on one consistent foundation with shared security, governance, and operational visibility. Where RPA is the right answer, the platform uses it. Where AI is, it uses that.
Yes, and we recommend it. The typical pattern is to build the platform foundations alongside one business area (finance is a common starting point) to prove ROI quickly. Once value is demonstrated, additional use cases roll out on the same foundation at a fraction of the cost. The platform compounds in value with each new deployment, so the first use case is the most expensive.
Yes. Enterprise integration is often central to what makes an AI platform useful operationally. We have experience with Microsoft Dynamics, SAP, custom ERPs, legacy databases, mainframe systems, document stores, and bespoke line-of-business applications. The integration layer is designed to handle the messy reality of real enterprise IT estates, including the systems your team would rather not talk about.
Our platforms are architected specifically to evolve with the AI market. A modular AI service layer means models and providers can be swapped without rebuilding the workflows and business logic that run on top. New AI capabilities are integrated when they make business sense, not when a vendor announces them. Long-term platform management, through Continuity or Managed Service, keeps the platform current as the market changes.
Security, governance, and access controls are built into the platform from day one, one consistent security model rather than fragmented, one-off implementations. The Microsoft Azure foundation provides an enterprise-grade security baseline; we can layer audit logging, transparent workflows, and human-in-the-loop controls when required. We have worked with regulated clients in fire safety and security, financial services, national infrastructure, and logistics where this is non-negotiable.
Three things. First, the contractual project success guarantee: we have never failed to deliver in over 10 years and we will guarantee yours will not be the first. Second, the Digital Transformation Platform and CambrianOS: a decade of frameworks, components, and proven patterns that accelerates every project. Third, a full in-house cross-disciplinary team (architects, AI engineers, data specialists, designers, QA, product) so the work is delivered as one team rather than coordinated across multiple suppliers.
Yes. Continuity Services provide ongoing technical support, monitoring, maintenance, and security management at multiple SLA levels. Managed Service models provide a dedicated long-term team that continues to develop new use cases on the platform quarter after quarter. You retain full ownership at every stage and can take the work in-house whenever you have the capability. The only tie-in to Steer73 is our quality.
Ready to talk about your enterprise AI platform?
Whether you are exploring whether an enterprise AI platform is right for your business, comparing approaches, or ready to scope a project, we’d like to talk. Discovery is free. No obligation, no sales pitch, just an honest conversation about what AI could do for your business and how to get there.