The pilot impressed everyone. Then it stalled.
The demo ran on clean sample data. Production needs your real data, your real systems and your real security rules, and that is where most AI projects stop.
AI readiness · AI consulting · Azure cloud
Make your software AI‑ready. Then put agents to work on it.
Most AI projects stall between the pilot and production. Our AI readiness assessment shows exactly what is blocking yours, from data and systems to security and cost, and our experts fix it with you.
- Shipping production software since 2013
- Founders with 25+ years in enterprise architecture and engineering leadership
- Working across the UK and South Africa
AI-ready architectureBuilt on Azure
- AI assistants & agentsChat · voice · copilots · automations
- AI gatewayCost limits · model choice · Azure OpenAI or private models05
- Safe actionsLimited access · preview before change · human sign-off03
- Your applicationsEvery request tied to one customer’s data01
- Data & knowledgeOne trusted source for every answer02
Microsoft Azure.NET / ASP.NET CoreContainer Apps · AKSEntra IDAzure OpenAISelf-hosted LLMsModel Context Protocoln8nBicep · Terraform · OpenTofu
The problem
AI is easy to demo and hard to put into production.
Your board wants AI. Your teams have tried it. And yet very little is running in front of customers, with the confidence to scale it. If that sounds familiar, the cause is rarely the AI model itself.
Nobody can sign off the risk.
Security, legal and compliance cannot approve what they cannot see: where customer data goes, what the AI is allowed to change, and who is accountable when it is wrong.
The bill is growing. The value isn’t.
AI costs rise with every user and every request. Without knowing what each customer and feature costs, nobody can say whether it is paying back.
The solution
An AI readiness assessment, led by people who build AI.
In two to three weeks our experts review your data, systems, cloud and team, then tell you exactly what stands between you and AI that works in production, and what it will take to get there.
What you walk away with
- Readiness score. Your organisation scored across six areas, with the evidence behind every score.
- Risks ranked by impact. Security, privacy, compliance, cost and delivery risks, in priority order.
- Target architecture. What your data, systems and cloud need to look like for AI to run safely.
- 90-day roadmap. What to fix first, in what order, with effort and cost estimates.
- Board-ready summary. A plain-language report your leadership team can decide on.
What we assess
Six questions every AI rollout has to answer.
Customer data separation
Can AI work for one customer without ever seeing another customer’s data?
- Each customer’s data walled off at every layer, including AI search
- Separate, revocable access for every AI integration
Data and knowledge quality
Is the information your AI relies on accurate, current and owned by someone?
- One approved source for every answer
- Content labelled with owner, status and last review
Safe system connections
Can AI take actions in your systems without being able to do damage?
- AI proposes, people approve the risky steps
- Every action limited, logged and reversible
Testing and release
Can you change an AI feature and prove it still works before customers see it?
- Automated quality checks on every AI change
- Repeatable environments defined in code
Cost and performance
Do you know what AI costs per customer and per feature, and whether it pays back?
- Spend tracked per customer and per feature
- Budgets and alerts before the bill surprises you
Security, risk and compliance
Would your AI pass a security review, an audit and a regulator’s questions?
- Defences against prompt injection and data leaks
- An audit trail a regulator would accept
Our expertise
Led by founders who have run engineering at enterprise scale.
Our founders have spent more than 25 years designing and running the platforms behind a global insurance and risk business, financial services, high-volume online gaming and health technology. Not as advisers, but as the people accountable for keeping them running. That is the experience behind every assessment.
25+ yearsof enterprise architecture and engineering leadership between our founders
2–3 weeksto an evidence-based readiness score and a costed AI roadmap
90 daysroadmap from assessment to your first AI use case live with customers
100%of AI actions logged and auditable, with human sign-off on anything high-risk
Set architecture for a global enterprise
Director-level enterprise architecture at a global insurance and risk business, on platforms that had to satisfy regulators, auditors and risk committees before anything shipped.
- For you: Your AI plan is built to pass your own risk and compliance sign-off.
Run engineering, not just advised on it
Head of Engineering, accountable for standards, delivery and production reliability on financial services and technology platforms.
- For you: Recommendations your teams can actually deliver, with realistic effort and sequencing.
Built systems where money must reconcile
Payments, collections, investment products and ledgers where every transaction is audited and nothing is allowed to charge twice.
- For you: AI that touches money is designed with the controls finance and auditors expect.
Scaled platforms under real-time load
High-volume online sports and casino platforms where milliseconds, uptime and fraud controls decide revenue.
- For you: AI costs and response times planned for your peak, not for the demo.
Protected sensitive data
Health technology and insurance platforms with strict privacy obligations and government stakeholders.
- For you: Privacy, data residency and access controls designed in from day one.
Shipped and run our own AI
Two AI products designed, built and operated on Azure end to end: architecture, security, cost control and support.
- For you: Advice already tested on real customers and real invoices.
Dane Hollenbach
Co-founder · Enterprise AI and cloud architecture
Enterprise AI and Azure architect with more than 12 years across financial services, insurance and health technology. His director-level career includes five years leading platform architecture at a global insurance and risk business, where accuracy, risk and compliance decide every design. Microsoft Certified Azure Solutions Architect Expert, published AI/ML researcher, and the architect of FAQ Hub, our AI help-centre platform.
Nemanja Spasic
Co-founder · Head of Engineering
Head of Engineering with more than 13 years of hands-on and leadership experience building platforms for financial services and technology businesses. He sets our engineering standards and leads delivery: architecture decisions, quality, reliability and the senior team that builds and runs client platforms.
Industries
Where getting AI wrong is expensive.
We focus on sectors where data, money or people’s wellbeing are on the line.
Financial services and fintech
AI in payments, lending, collections and investments must be explainable, auditable and never the only control on money.
How we help →SaaS and software companies
Customers expect AI features now. Shipping them without mixing up customer data or doubling hosting costs is the hard part.
How we help →Health and care
Patient and clinical information demands privacy, accuracy and human oversight that most AI tools do not provide out of the box.
How we help →Gaming and high-volume platforms
Real-time systems where milliseconds, uptime and fraud controls decide revenue, and AI has to keep up without adding risk.
How we help →Education
Student support, admissions and knowledge services at scale, with accuracy and safeguarding that cannot be left to chance.
How we help →Logistics and operations
Quotes, bookings and customer service spread across systems that were never designed to work together, let alone with AI.
How we help →Services
From assessment to AI that runs your business.
The assessment tells you what to do. These services get it done, and leave you with something your own teams can run.
01
AI Integration & Agentic Workflows
AI that works inside your rules, not around them.
Put AI to work inside your products and operations: assistants that answer from your own data, agents that take approved actions in your systems, and automations that connect them, with quality checks, cost limits and human sign-off built in. Managed or private models, your choice.
Explore the service →02Enterprise Platform Engineering & Azure Cloud Architecture
A cloud platform where the secure way is also the fast way.
We design and build Azure foundations (networking, identity, infrastructure as code, deployment pipelines and monitoring) so your teams ship AI features in days, not quarters, and every pound of cloud spend is accounted for.
Explore the service →03Fintech & High-Compliance Software Development
Software that survives an audit as well as a traffic spike.
Payments, collections, investment products and financial back-office systems built the way regulators expect: no double charges, every transaction reconciled, a full audit trail, data kept where it must stay, and access limited to who needs it. That includes the AI now running inside them.
Explore the service →04SaaS Platform Modernisation
From a separate copy per customer to one platform that serves them all.
Many SaaS products still run a separate copy for each customer, which makes every release and every AI rollout slower and more expensive. We move you to one shared platform that keeps every customer’s data strictly separate, even when AI is doing the work, with usage-based billing built in.
Explore the service →Proof, not promises
Our advice comes from AI we run ourselves.
We build and operate our own AI products. Everything we recommend has already faced real customers: answers that cite their sources, AI with firm limits, people in control and costs that stay predictable.
For B2B SaaS and support teams
FAQ Hub
Help centres and in-app AI assistants built from a company’s own content. One platform serving many businesses, answering only from approved sources, running on Azure.
- Answers it can prove. Every answer cites its source; questions it cannot answer go to a person with the context attached.
- AI that cannot bypass review. AI tools can draft and plan changes, but publishing always needs an explicit, recorded approval.
- Built on Azure. ASP.NET Core on Container Apps behind Front Door and WAF, Azure SQL, Blob Storage and Azure AI.
For UK tradespeople and small service businesses
Max & Mia
AI employees that answer the calls a tradesperson cannot take. Max, the phone assistant, answers from approved business details; Mia, the back-office assistant, hands the owner a reviewable callback job card.
- Voice AI with hard boundaries. Max speaks only from approved business details and never promises a price, booking or attendance time.
- An honest handover. Mia’s job card separates what the caller said from what is still unknown, so gaps stay visible.
- The owner stays in control. Call forwarding is the owner’s choice, and every commitment is made by a person on the callback.
Track record
Trusted with production systems since 2013.
Before “AI-ready” was a category, we were building the systems AI now depends on: websites, internal applications, integrations and SaaS for organisations in education, digital health, financial services, logistics, construction and hospitality.
That work taught us what decides whether AI succeeds: clean data, systems that connect reliably, access that holds and releases that do not break on a Friday.
AI changes what software can do. It does not change what makes software trustworthy.
How we work
From assessment to AI in production.
Fixed-scope stages, delivered into your systems and demoed every two weeks. No black boxes.
AI readiness assessment
2–3 weeksWe review your data, systems, cloud and team against six readiness areas. You get a score, ranked risks, a target architecture and a 90-day roadmap with costs.
Fix the foundations
2-week sprintsWe remove the blockers the assessment found: data, access, pipelines and guardrails. Fixed scope per sprint, demoed and delivered into your systems.
Put AI to work
First use case liveWe take the first AI use case into production with quality checks, monitoring and cost controls in place from day one, not after an incident.
Hand over and scale
Until you own itWe run it alongside your team, then hand over documentation, infrastructure code and test suites. The goal is your independence, not a retainer.
Insights
Field notes from production AI.
Practical guidance from our own builds, for leaders and engineers alike.
What AI Readiness Actually Means, and How to Measure It
Your organisation is AI-ready when adding or changing an AI feature is routine, reviewable and reversible. Six areas to measure it, with the evidence to look for.
Read the article →A Paved Road for AI Workloads on Azure
The Azure platform defaults we set before a team ships its first AI feature: managed identity, private model endpoints, an AI gateway, tracing, cost attribution and eval gates.
Read the article →Agentic Workflows in Regulated Fintech: Guardrails That Survive an Audit
How to put AI agents into payments, collections and lending workflows without creating reportable incidents: propose-approve, idempotency, decision records and residency.
Read the article →Find out what is stopping your AI from reaching production.
In two to three weeks, our AI readiness assessment gives you a score across six areas, your risks ranked by impact, a target architecture and a costed 90-day roadmap.