Your data is the moat. The model is interchangeable. We build the product around the data nobody else can collect, your customers’ history fed into a RAG layer competitors can’t replicate without years of data, not around one provider’s API.
Service AI product development · AI-native + AI-first engineering · UK
AI product development that survives if we turn. the AI off.
AI product development for UK founders building AI-native businesses. Bolting GPT into a feature is a sprint — building an AI-first product is a different engineering job. We build the full product from week one: data layer, RAG, evals, cost discipline, gateway and fallback paths. Not a feature. A product.
8AI-native products shipped
71%median gross margin
3.4×avg valuation lift
The founder this page is for
“What’s your product if we turn the AI off tomorrow?”
Nadia had been raising for three months. The fourth VC asked that one question and the conversation was over. Her SaaS was a project tool with three AI features bolted on, a worse Asana without them. We rebuilt it AI-native in 14 weeks: the data layer was the moat, the agent took actions, the cost layer worked at 50 customers and 5,000. The round closed at 3.4× the previous valuation.
“The fifth VC asked the same question. This time we had an answer. Round closed eight weeks later at 3.4× the previous valuation.”
001
0
AI-first products shipped to UK founders since 2023.
002
0%
Median gross margin after our cost-discipline ladder.
003
0%
Median eval pass rate at launch across shipped AI products.
What AI-first engineering actually meansdata is the moat · the model is interchangeable
The product still works if your model vanishes. That’s the whole game.
The agent takes actions in tools your customers already live in. The cost layer is wired so unit economics work at 50 customers and at 5,000. The eval set proves quality on every release. If OpenAI vanished tomorrow, the product still works on Claude or self-hosted Llama.
This page is for founders who don’t want the fifth VC to ask the same question Nadia’s fourth one did.
An AI feature is not an AI product.
Bolting GPT into a tab is a sprint. Building a product that’s defensible without one model is a different job.
Bolt-ona worse Asana with AI
What it looks like
- SaaS with a chatbot tab
- A “Generate” button
- One OpenAI call deep
- No evals
- Costs hidden in the cloud bill
Nativedefensible without one model
What we build
- The AI is the product
- Data layer + RAG + evals
- Multi-model gateway
- Cost = unit economics, measured
- Survives if a provider vanishes
Are you building an AI product,
or an AI feature?
Three or more “no”s and you’re building a feature, not a product. That changes who builds it, how it’s funded, and what defensibility looks like. Open any row.
Customer history, proprietary signals, behaviour data. If anyone with an OpenAI key can replicate your output, the model isn’t your moat, the data is.
Eval gold set, 100-300 tasks with known answers, run on every release. If quality is “feels right”, you’re not engineering, you’re vibing.
An assistant that drafts is a feature. An agent that schedules + sends + reconciles is a product. Tool-use changes the engineering and the business model.
Cost-per-call × calls-per-user × users. If you break even at 50K MAU but lose money at 500K, the architecture isn’t a product yet.
A gateway abstraction means you survive a 4× price hike or a Friday deprecation. If you’re tied to one provider, you don’t own the runtime.
The four-layer AI product spine
Same shape, every project
Every AI product we ship is built on the same four layers. The data is the moat, the gateway is the insurance, the tool-use is the business model, the evals are the proof.
Layer 01
Data + retrieval
The customer-specific data the AI thinks against. Indexed in Pinecone / Weaviate / pgvector. Re-ranked. Freshness windowed. Your moat lives here.
Layer 02
Gateway + routing
One module wraps every provider. Per-call budget. Schema validation. Per-tenant tracking. Swap GPT-5 to Claude to self-hosted Llama in config.
Layer 03
Tool-use + actions
The AI takes actions in your real systems: write to DB, post to Slack, schedule jobs. MCP servers where it helps. Audit log every action.
Layer 04
Eval + observability
Gold set runs on every PR. P95 latency, cost per call, accuracy per tenant tracked live. Replay any production trace. No silent regressions.
The pain. The architectural answer.
Each one decides whether your AI product survives the next VC question, the next price hike, or the next 10× in scale.
- 01
“What’s your product if we turn the AI off tomorrow? We don’t have an answer.”
Data + UX moat first, model second. Your data is the defensibility. Models are interchangeable through the gateway. If OpenAI vanished, your product still works on Claude or self-hosted Llama.
- 02
“We make money at 50 customers and lose money at 500.”
Per-task model routing + cost discipline. Haiku for 60%, Sonnet for 35%, Opus for 5%. Same UX, 1/4 the cost per call. Median gross margin post-discipline: 81% vs 24% before.
- 03
“We can’t tell if a new prompt is better. We just deploy and hope.”
Eval gold set + CI gating. 100-300 tasks with known answers. CI blocks deploys regressing more than 5%. Quality stops being “feels right” and starts being a number.
- 04
“OpenAI doubled prices and we have to swallow it.”
Multi-model gateway, swap in config. Swap GPT-5 to Claude to Gemini to self-hosted Llama in a config change. Verified against your gold set before cutover. Done four times for clients in 2025.
- 05
“Two customers are 60% of our LLM bill. We have no idea which.”
Per-tenant + per-feature cost log. Every LLM call logged with tenant, feature, token count, GBP cost. Live dashboard. Anomaly alerts to Slack. Pricing decisions become data, not guesses.
- 06
“The AI invented a customer policy. Now we have a real complaint.”
RAG with citations + confidence threshold. No citation, no answer. Confidence threshold + escalation to human. Replay any production hallucination, add it to the gold set so it never happens again.
- 07
“Will OpenAI train on our customers’ data?”
Zero-retention tier + PII redaction. Zero-retention tier by default on every provider. PII redaction at the gateway. For regulated data we self-host Llama or Mixtral on your VPC. DPA before week one.
- 08
“The demo is beautiful, but it can’t handle a real customer load.”
Production-shape architecture from week one. RAG, gateway, evals, observability built in week two. The demo IS the production system, just with fewer customers. No “rebuild for launch” sprint.
WHY AI-NATIVE FOUNDERS SIGN
Four things that turn a demo into a defensible product
The engineering that makes the difference between a clever feature and a fundable AI company.
Multi-model gateway
OpenAI, Anthropic Claude, Google Gemini, self-hosted Llama, routed per task. Swap providers in config. No lock-in.
Eval gold set on every PR
100-300 representative tasks with known answers. CI blocks deploys that regress more than 5%. Quality is a number.
Per-tenant cost attribution
Every LLM call logged with tenant + feature + token count + GBP cost. Your CFO has answers your competitors don’t.
RAG that holds up
Pinecone or Weaviate vector store. Re-ranking. Freshness windowed. Your data is the moat, not the model.
The stack we ship every AI product on.
MERN for the product surface, Python for inference and evals, and the AI infrastructure layer that keeps the unit economics working at scale.
T1
What we build every AI product on
MERN + PythonMongoDBExpress.jsReact + Next.jsNode.jsPythonTypeScriptAnthropic ClaudeOpenAI GPT-5PineconeLangChain / LangGraphMixpanelStripe
T2
When your AI brief calls for it
reach when neededLlama / MixtralWeaviate / QdrantPostgreSQL + pgvectorApache SparkFastAPIHuggingFace
T3
The cloud + infra
AWS-defaultAWSAWS BedrockAWS LambdaAWS SageMakerKubernetes (EKS)DockerRedisApache KafkaTerraformDatadog + SentryLangSmithMCP servers
What AI-founders actually ask before signing
Pain-first, soft-second.
Both, behind a gateway. Sonnet 4.6 for default reasoning + tone, Haiku for cheap routing + classification, Opus for the weekly heavy lift. GPT-5 for vision, voice, image generation. The gateway routes per task. You never depend on one provider. If Anthropic doubles prices or OpenAI deprecates an endpoint, you flip a config flag.
The eval gold set. We build 100-300 representative tasks with your team in week one. Every PR runs against it. Quality is a number you both track. You launch when the number says it’s ready, not when the team feels brave. The number ships with the runbook.
Usually unnecessary in 2026. Sonnet + your RAG gets to within 2-3% of a fine-tuned model for most tasks, with 90% less ops cost. We’ll fine-tune Llama on your data only when the eval shows a clear gap a hosted model can’t close. Roughly one project in five.
Zero-retention tier on every hosted provider by default. PII redaction at the gateway. For regulated data we self-host Llama or Mixtral on your VPC. Signed DPA + sub-processor list before week one. Every audit we’ve faced has passed.
30-day walk-away both ways. Milestone billing 25/25/25/25. UK VAT registered, listed on Companies House, shipping since 2019. You’re never more than four weeks at risk of paying for nothing.
We model unit economics at 100× current scale on day one. Cost-per-call × calls-per-user × users. If your subscription is £49 and AI cost is £9.20 per user/month, you have 81% gross margin. If those numbers don’t work, we tell you in the audit, not in week ten.
Every AI product development project has two senior engineers paired, not one. Every decision goes into an ADR the same day. Mohit reviews every PR. In seven years, two engineers have left mid-project. Both handovers were inside 48 hours.
Yes, with 14 days’ notice. Engineers move to other projects. Spend pauses. Resume with 14 days’ notice. No cancellation fee. Done six times in 2025.
The AI product
AI features users trust.
Structured outputs, evals on every release, audit logs and graceful fallback — AI that survives past the first impressive demo.
One paragraph. That’s it.
Tell us what the AI does, what data feeds it, and your honest answer to the VC question. Mohit replies inside 24 hours: a clear yes, a clear no, or the one question that decides it.
What happens after the email lands
- < 24h
A personal reply.
Yes, no, or the deciding question. Straight to your inbox.
- Week 1
The diagnostic.
We answer the five questions with you and draw the four-layer spine.
- Wk 14
A defensible product.
Data moat, gateway, evals, the brief your next investor reads.