AI that works
in production.
RAG systems, LLM integrations, and agentic workflows — built to production standards, not demo standards. We make your SaaS meaningfully smarter, not just AI-branded.
The problem
Most 'AI features' are a ChatGPT wrapper with a company logo. No retrieval pipeline, no evaluation framework, no cost controls — and users lose trust the moment the AI hallucinates.
Our approach
We build an evaluation framework before writing a prompt. Every AI decision — model choice, chunking strategy, retrieval method — is measured against your success metrics, not shipped on vibes.
The result
AI features that earn user trust because they're accurate, fast, and predictable. With observability and regression testing so the system improves over time instead of drifting.
The full AI
stack, production-ready.
Not just an API call. A complete, observable, cost-controlled AI system built to run reliably at scale.
RAG pipeline
Chunking, embedding, retrieval, reranking
Vector database setup
pgvector, Pinecone, or Weaviate
LLM integration
OpenAI, Anthropic, or OSS models
AI agent workflows
Tool use, memory, multi-step reasoning
AI safety guardrails
Output validation, hallucination reduction
Evaluation framework
Metrics, golden datasets, regression tests
CI/CD for AI pipelines
Prompt versioning, eval gates
Scalable cloud deployment
AWS Lambda, ECS, streaming APIs
3 months post-launch support
Model updates, monitoring, iterations
From idea to
production AI in 7 weeks.
An eval-first process that measures quality at every step — so you know the AI is improving, not just changing.
AI Discovery
We map the business problem, data sources, and success metrics before touching any model. We leave with a clear architecture — which AI approach, which models, what data is needed, and how we'll measure success.
Deliverables
- AI problem framing doc
- Architecture decision record
- Data audit & quality assessment
- Success metrics & evaluation plan
Data Pipeline & Embeddings
We ingest your data sources, design the chunking strategy, generate embeddings, and set up the vector database. The retrieval quality at this stage determines the quality of everything built on top of it.
Deliverables
- Data ingestion pipeline
- Embedding model selection
- Vector database (pgvector / Pinecone)
- Retrieval quality benchmarks
Core AI Feature Build
Iterative development of the AI feature — prompt engineering, RAG pipeline tuning, agent tool design, or LLM integration. We run evals at every iteration so improvement is measurable, not subjective.
Deliverables
- Core AI feature (working)
- Prompt library & versioning
- Evaluation harness
- Latency profiling
Safety, Guardrails & QA
Output validation, hallucination rate testing, adversarial prompt testing, and cost profiling. We make sure the system behaves predictably in edge cases before it touches real users.
Deliverables
- Output validation layer
- Adversarial test suite
- Cost-per-query analysis
- Safety evaluation report
Launch & Observability
Production deployment with streaming, caching, and rate limiting. Full observability setup — latency, token usage, user feedback loops, and automated regression testing on new model versions.
Deliverables
- Production deployment
- LLM observability (LangSmith / Helicone)
- Cost alerting
- Feedback loop for continuous improvement
Best models.
Best tooling.
We stay current with the AI ecosystem. When a better model or tool ships, we evaluate it against your production evals before recommending a switch.
Four AI disciplines,
one team.
AI that knows your business data.
RAG & Knowledge Systems
Retrieval-Augmented Generation systems that let your users ask questions about your documents, knowledge base, or product data — and get accurate, cited answers. Built with production-grade retrieval pipelines, not toy demos.
- Document ingestion — PDF, HTML, Markdown, DB
- Semantic chunking and embedding pipeline
- Hybrid search — vector + keyword (BM25)
- Reranking for accuracy (Cohere Rerank / cross-encoder)
- Citation tracking so answers are verifiable
GPT-powered features, built into your product.
LLM Integration
We integrate large language models into your existing product as first-class features — not bolted-on chatbots. Structured output extraction, function calling, streaming UIs, and production-grade error handling.
- Structured output with Zod / JSON Schema
- Function calling / tool use for actions
- Streaming responses with backpressure handling
- Prompt versioning and A/B testing
- Multi-provider fallback (OpenAI → Anthropic)
Autonomous workflows that actually finish tasks.
AI Agents & Automation
We build AI agents that can reason across multiple steps, use tools, access external data, and complete multi-step tasks — with proper error recovery and human-in-the-loop checkpoints where reliability matters.
- LangGraph for stateful, multi-step agent workflows
- Tool use — web search, code execution, APIs
- Memory: short-term (conversation) + long-term (vector)
- Human-in-the-loop approval for destructive actions
- Cost control via token budgets and early stopping
AI that makes your SaaS meaningfully smarter.
Custom AI Features
Embedding AI directly into your product's core features — smart search, intelligent recommendations, automated categorisation, and content generation. Features that make your product feel fundamentally better, not just 'AI-powered'.
- Semantic search replacing keyword search
- Personalised recommendations engine
- Automated content generation and summarisation
- Intelligent data extraction from unstructured text
- AI-assisted onboarding and user guidance
For products where
AI creates real value.
SaaS Products
You want AI to make your product meaningfully better — smart search, intelligent recommendations, auto-categorisation, or AI-assisted workflows — not just a chatbot in the corner.
- —Large amounts of structured or unstructured data
- —Users spending time on repetitive tasks AI could automate
- —Competitors shipping AI features and you need to respond
Knowledge-Heavy Businesses
Your team spends too much time searching for information across documents, emails, and knowledge bases. You want an AI that can find and synthesise that information instantly.
- —Large document or knowledge base libraries
- —Support teams answering the same questions repeatedly
- —Compliance or legal content that needs reliable retrieval
Process-Heavy Operations
You have multi-step workflows that involve classification, extraction, routing, or summarisation. AI agents can take over these workflows — freeing your team for higher-value work.
- —Manual data entry or classification at scale
- —Multi-step approval or routing workflows
- —Report generation from structured or unstructured data
How do you prevent hallucinations in RAG systems?
Which LLM providers do you use?
Can you build an AI agent that takes actions, not just answers questions?
How do you handle AI costs at scale?
Do you work with private data without sending it to OpenAI?
How do you measure whether the AI feature is actually working?
Let's build your
AI feature together.
Tell us about your AI use case. We'll get back within 24 hours with a clear approach, timeline, and transparent pricing.