AI that works in production.
LLM integrations, RAG systems, and autonomous agents built for real business outcomes — not demos. We bring production engineering discipline to AI so the results hold up past the first week.
What we deliver
Eight capability areas across the full AI engineering stack — from raw data ingestion to monitored production systems.
LLM Integration
Production-ready integrations with Claude, OpenAI, Gemini, and locally hosted models (Ollama, vLLM). We handle model selection, fallback logic, and cost guardrails so your team is not guessing.
Retrieval-Augmented Generation
RAG pipelines that ground language models in your private data. Chunking strategies, embedding pipelines, hybrid search, and reranking — tuned until retrieval accuracy meets your production bar.
Autonomous Agents
Multi-step agents that plan, use tools, and complete tasks with minimal human intervention. Built on LangChain or custom orchestration, with structured outputs and auditability baked in.
Vector Search & Databases
Pinecone, pgvector, and Qdrant deployments sized for your dataset. We design the embedding strategy, index configuration, and metadata filtering your queries actually need.
Document Intelligence
Structured data extraction from PDFs, scanned documents, contracts, and forms at 94% accuracy or higher on production pipelines. Replace brittle rule-based parsers with model-backed pipelines.
Prompt Engineering & Evaluation
Systematic prompt design with automated evaluation harnesses. We track regression across model versions so a provider update never silently breaks your product.
Data Pipeline & Embeddings
ETL pipelines that clean, chunk, and embed your unstructured data on a schedule. Incremental updates, deduplication, and freshness guarantees for RAG and search use cases.
Production Hardening
Rate limiting, PII redaction, output validation, cost budgets, and observability tooling. We treat AI systems with the same operational rigour as any critical backend service.
How we work
A structured process that produces measurable results, not impressive slide decks.
Use-Case Scoping
We map the specific decision or workflow you want to automate, audit your existing data sources, and define a measurable success criterion before any model is selected. Many projects fail because this step was skipped.
Data Pipeline & Embedding
We build the ingestion and transformation layer: cleaning raw documents, applying appropriate chunking, generating embeddings, and loading them into a vector store with the right metadata schema for your retrieval patterns.
Model Integration & Evaluation
We wire in the LLM, design the prompt architecture, and run structured evaluation against a golden dataset. Accuracy, latency, and cost per query are all tracked and optimised before any code ships to staging.
Production Hardening
Monitoring dashboards, alerting on accuracy drift, token budget enforcement, and a deployment runbook. We also run a red-teaming pass to surface prompt injection or data-leakage risks before launch.
Tech stack
We work with the leading AI infrastructure tooling and select models based on task requirements, not brand preference.
This is right for you if...
Companies sitting on years of unstructured data — reports, emails, contracts, support tickets — that nobody can query or act on at scale. We turn that backlog into a searchable, AI-queryable asset.
Teams that spend hours each week on repetitive knowledge work: summarising documents, drafting responses, classifying inputs. We scope and build the automation that gets those hours back.
Products that need intelligent search, personalised recommendations, or context-aware responses — and cannot afford the latency or cost of naive LLM calls on every request.
Ready to put AI to work?
Share your use case and your data landscape. We will scope a pilot within 48 hours and show you what is achievable before you commit to a full build.
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