Sound familiar?
AI challenges that stall startups before they start
The gap between a ChatGPT demo and a production AI feature is wider than most teams expect. Here is what we see most often.
Hallucinations & unreliable outputs
We design grounded systems with retrieval, structured outputs and automated eval pipelines that keep accuracy within tolerance for your use case.
PoC that never reaches production
We scope PoCs with production viability in mind from day one — latency budgets, cost models and security baked in, not bolted on later.
Spiralling API costs
Caching strategies, model routing, prompt compression and selective use of smaller models bring inference costs under control without sacrificing accuracy.
Free tool How much will your AI agents cost to run? →No AI strategy or roadmap
We run structured feasibility reviews to identify the highest-ROI AI opportunities before a line of code is written.
What we offer
AI engineering capabilities from PoC to production
Six capability areas designed to move AI from experiment to a reliable, measurable part of your product.
Context engineering & LLM integration
Design the full context window — system prompts, retrieved chunks, tool schemas and conversation history — then integrate GPT-4o, Claude, Gemini or open-weight models with structured outputs and fallback logic.
- Context window architecture & prompt design
- Structured outputs, function calling & retries
RAG pipelines & knowledge bases
Retrieval-augmented generation over your documents, databases or APIs — so your AI answers with your own knowledge, not hallucinated facts.
- Embedding pipelines & chunking strategies
- Hybrid search & re-ranking
Agentic AI & loop engineering
Design and build agentic systems with plan→act→observe loops, tool orchestration and multi-step reasoning — plus the harness engineering and human-in-the-loop checkpoints that make them production-safe.
- Loop design, tool orchestration & stopping conditions
- Agent harness, sandboxed execution & audit trails
MLOps & model deployment
Scalable inference infrastructure, model versioning, A/B testing and monitoring — so you can iterate safely on live AI features without downtime.
- Managed inference on AWS, Azure or GCP
- Evals, drift detection & automated alerts
AI-powered data pipelines
Automated extraction, transformation and enrichment using AI for classification, entity extraction and summarisation at scale.
- Document parsing & structured extraction
- Batch & streaming pipeline support
AI feasibility & strategy
A structured review of your use case, data quality, expected ROI and technical risk — giving you a clear recommendation before you invest.
- Use-case prioritisation matrix
- Cost & accuracy projections
How we work
An AI delivery model built for production, not demos
We do not hand you a notebook and call it done. Every AI engagement ends with something observable, measurable and maintainable.
Assess
Understand your use case, data, cost tolerance and accuracy requirements — then scope the right approach before writing a prompt.
Prototype
A working PoC with baseline evals to prove the approach is viable before committing to full build and production infrastructure.
Build
Production-grade implementation with observability, error handling, cost controls and security review — integrated into your existing stack.
Optimise
Ongoing eval runs, latency profiling, cost reduction and model upgrades — so your AI improves as models and your data evolve.
What you receive
Concrete AI deliverables at every stage
No throwaway prototypes. Every engagement produces assets your team can own, measure and build on.
Feasibility & assessment
- Use-case prioritisation & ROI model
- Data readiness & quality report
- Model selection recommendation
PoC & integration
- Working prototype with baseline evals
- Latency & cost benchmarks
- Security & prompt injection review
Production system
- Deployed, monitored AI integration
- Eval harness & runbook
- Knowledge transfer & handover docs
Outcome-based pricing
Tell us the outcome.
We'll price to it.
Fixed base fee to deliver. Variable fee paid only if the metric lands — we carry that risk.
Outcomes we price to
Baseline and target agreed during scoping. Measured at 60–90 days post-delivery using your own data.
Technology
Modern AI engineering stack, model-agnostic approach
We choose the right model and framework for your use case — not the most hyped one at the time.
Foundation Models
OpenAI GPT-4o
Anthropic Claude
Google Gemini
Llama / Mistral
Frameworks & Orchestration
LangChain / LangGraph
LlamaIndex
CrewAI / AutoGen
Python / FastAPI
Vector Databases & Search
Pinecone
Weaviate
pgvector
Elasticsearch / OpenSearch
Infrastructure & Monitoring
AWS Bedrock / Azure OpenAI
Google Vertex AI
Langfuse / Helicone
Modal / Replicate
Engagement models
AI projects sized for where you are now
Start with a low-commitment assessment, prove the PoC, then scale with confidence.
Fixed price
AI feasibility review
30-minute call plus a structured report covering use-case viability, data readiness, model options and estimated cost — delivered within 48 hours.
Time-boxed
Proof of concept
2–4 weeks to deliver a working prototype with baseline evals, a cost model and a clear recommendation on whether to proceed to production.
Monthly retainer
Production AI engineering
Ongoing AI development, monitoring, eval improvement and model upgrades — billed monthly with a capped sprint budget and named delivery lead.
Not sure about costs yet?
Estimate your AI agent build before committing to a call.
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Let's talk
Book a free 30-minute discovery call
Tell us about your product, your data and the AI outcome you are trying to achieve. We will be honest about what is realistic and how we would approach it.
- No obligation — just a conversation
- Feasibility report within 48 hours
- PoC can start within 2 weeks of sign-off
Not ready to book? Browse the Playbook first →