Which model will you use?

The one that passes your benchmark at the lowest cost. We run the evaluation set against several current models during week 1 and show you the scores, latency and cost side by side before committing.

Can our data stay inside our environment?

Yes. Zero-retention API terms are the default, and for stricter requirements we deploy open-weight models on your own infrastructure with egress to external APIs blocked at the network layer.

We already built a RAG prototype that is not accurate enough. Can you fix it?

That is a common engagement. We start by measuring retrieval recall on 50 real questions, which usually locates the problem in a week, then fix chunking, retrieval and the grounding gate in that order.

Generative AI Development

LLM applications and RAG systems that still answer correctly 6 months after launch

Retrieval that finds the right passage, generation that cites it, and a backend with authentication, multi-tenancy and usage metering. Evaluated against a ground-truth benchmark before anything reaches a user.

Book a free scoping call See the work

91%: Factual accuracy, clinical RAG platform
61%: Better retrieval precision vs prior tooling
8K+: Daily queries on 1 knowledge platform
3: Languages in 1 compliance system

What we build

Generative AI Development, delivered as a working production system

Scoping, architecture, build, evaluation, deployment, monitoring and handover under 1 engagement, delivered by the engineer who designs it.

RAG and document intelligence

Search, understand and answer from PDFs, databases, CRMs, contracts, support tickets and internal wikis. Hybrid BM25 plus dense retrieval, cross-encoder reranking, citation grounding and role-based access come standard.

LLM SaaS and internal copilots

The whole product: backend APIs, authentication, admin dashboards, usage analytics, database schema and cloud deployment. Copilots for support, sales, HR, legal and operations teams that staff keep using after week 2.

Knowledge graphs and multi-hop reasoning

Neo4j graph expansion and RAG Fusion for questions that span many documents, where single-vector search has already failed.

Multilingual and regulated deployments

English, German and French compliance reporting across 8 jurisdictions; HIPAA clinical decision support with a full audit trail on every response.

Evaluation harness you can rerun

A 50-plus question labelled benchmark built in week 1, RAGAS and retrieval recall tracked per release, and a grounding gate that blocks uncited answers.

Private and zero-retention options

Zero-retention API terms by default. Where nothing may leave the environment, open-weight models on client-owned GPUs inside your VPC.

Architecture

The retrieval pipeline we ship by default

Structure-aware chunking feeds a hybrid index. Each query is scoped by the caller’s permissions, retrieved, reranked by a cross-encoder and checked by a grounding gate before the model answers with citations. Every step is traced, so a wrong answer can be replayed to the exact chunk that caused it.

Where these projects usually fail

The 4 mistakes we get called in to fix, and what we do differently

Retrieval recall nobody measured

Most RAG failures are retrieval failures. If the right passage is not in the top k, no prompt can recover it. We measure recall on real questions before touching generation.

Chunking that cuts tables from headers

Fixed 512-token windows split contract clauses from their definitions. We chunk by document structure and carry titles and section paths into every chunk.

Dense-only search on identifiers

Vectors are weak on codes, part numbers and references. Hybrid retrieval with reranking is the cheapest precision gain available and it is in every build.

A demo with no backend

A chat box over a notebook is a prototype. We ship auth, tenancy, metering, rate limits and dashboards so the system can be sold or rolled out.

Proof

Delivered systems in this practice area

Every figure comes from a system running in production for a real client.

HealthcareUSA

HIPAA-compliant clinical decision support for a hospital network

1. Problem. A US hospital network had patient records, lab results and ICD mappings sitting in silos, none of it reachable at the point of care. Clinicians needed decision support without adding friction to already demanding workflows.

2. Solution. A HIPAA-compliant clinical platform on GCP Vertex AI using a hybrid Agentic RAG pipeline. Patient records, lab data and medical literature unified into one queryable layer. Role-based access across every clinical and admin role, a full audit trail on every response, and a zero-downtime SLA.

Agentic RAGGCP Vertex AIHIPAAFHIR

3. Impact

91%: Factual accuracy in clinical responses
27%: Reduction in patient onboarding time
48%: Less manual clinical documentation
HIPAA: Compliance built into the architecture

Enterprise SaaSUSA and UK

Knowledge search that handles multi-document reasoning at scale

1. Problem. Critical institutional knowledge was scattered across hundreds of documents and disconnected systems. A single-vector-search setup had already failed. The organization needed multi-document, multi-hop reasoning that held up under real query volume.

2. Solution. Multi-agent reasoning with a Neo4j knowledge graph for complex cross-document queries. RAG Fusion lifted precision well above single-retrieval approaches. Role-based access with a complete audit trail across every department, handling 8,000 queries per day at launch.

LangGraphRAG FusionNeo4jQdrant

3. Impact

54%: Faster knowledge retrieval
61%: Better retrieval precision vs prior tooling
37%: Fewer support tickets from knowledge gaps
8K+: Daily queries across departments

All 13 case studies

Stack

What we build on, and why it is current

Model names and tooling are kept up to date with what we are shipping this quarter. The selection logic is simple: the option that passes your benchmark at the lowest cost wins, and that is tested rather than assumed.

Models

Claude Fable 5.1Claude Opus 5.5Claude Sonnet 5.5GPT-6GPT-5.6Gemini 3.8Gemma 3Qwen 3.8DeepSeek V4.1

Orchestration

LangGraphLangChainClaude Agent SDKOpenAI Agents SDKPydantic

Retrieval

QdrantPineconepgvectorNeo4jElasticsearchCohere Rerank

Backend

FastAPIPostgreSQLRedisReactNext.js

Evaluation

RAGASLangfuseLangSmithArize Phoenixgolden test sets

Questions

Before you book a call

Agentic AI and AutomationAgents, MCP, orchestration and n8n workflows with approval gates AI/ML EngineeringModel development, fine-tuning, forecasting and computer vision Cloud and MLOpsAWS, Azure and GCP infrastructure, monitoring and CI/CD

A focused build over 1 or 2 data sources ships in 2 to 4 weeks, including the evaluation benchmark, deployment into your cloud and documentation. Multi-source platforms with access control and dashboards run 8 to 16 weeks.

Your AI system should work, not just demo well

Book a free 30-minute scoping call. You leave it with a recommended approach, a realistic timeline and a cost range, and you talk to the engineer who would design the system.

Book a free scoping call Send project details

Written scoping summaryyours after the call

1Recommended approachRAG with hybrid retrieval, or classical ML, or plain software. Including the option of not building.
2What the system ownsThe steps it resolves alone, and the steps that stay with a person.
3First metric and baselineThe number it has to beat, measured on your own data before any build.
4Timeline and cost rangePhased, with the first scored result inside 14 days.

Written by the engineer who would build itFree, no obligation, yours to take to any vendor

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