Your AI prototype works in the demo. It falls apart in production. That is the part we build.
We design and ship RAG systems, AI agents and LLM products that hold up when real data, real users and real compliance requirements hit them. Documented architecture, working backend, cloud deployment and a clean handover. You own all of it.
Six things we do at a level most teams cannot match
We are not generalists or prompt writers. We are AI engineers who have put production systems in front of real users across six technical practice areas.
RAG systems that retrieve the right passage
Most RAG projects fail at the retrieval layer, not the model. We build with hybrid search (BM25 plus dense vectors), cross-encoder reranking, citation grounding, and RAGAS evaluation against ground-truth benchmarks before anything goes live. Your knowledge base becomes searchable in seconds, with sources attached.
AI agents that complete real tasks
Not chatbots. Agents that connect to your APIs, databases, and tools, then take action and report back. We build on LangGraph and CrewAI with human approval gates, retry logic, and full trace observability, so you always know what the agent did and why it did it.
Workflow automation that runs around the clock
We self-host n8n inside your own infrastructure, so your data never leaves the building. One platform, 400 plus integrations, AI decision logic baked into every workflow. No per-operation pricing, no vendor lock-in, no surprise downtime.
LLM products with a real backend
Full AI products from scratch. Backend APIs, authentication, multi-tenancy, usage tracking, dashboards, database design, and cloud deployment, all in one engagement. You ship a product in weeks, not quarters, and you own the whole stack at the end.
MLOps built for regulated environments
Full-lifecycle ML infrastructure: training pipelines, model registries, shadow deployment, statistical drift detection, A/B testing, and CI/CD. Certified on AWS SageMaker, Azure ML, and GCP Vertex AI, with audit-ready lineage from the first commit.
Fine-tuning with ground-truth validation
We fine-tune domain-specific models on your proprietary data using QLoRA and DPO alignment. Every model is validated against held-out benchmarks with ROUGE, BERTScore, and citation accuracy before it touches a single production request.
Sometimes the honest answer is that you do not need a large language model
Most AI vendors have one hammer. We have shipped enough production systems to know that a generative model is the wrong tool at least as often as it is the right one, and we will say so on the first call rather than three months into a build.
Where generative AI quietly loses
Structured tabular prediction, demand forecasting, credit and fraud scoring, anomaly detection and anything that needs a stable, explainable decision boundary. A gradient boosted model on clean features usually beats an LLM on accuracy, latency and cost, and it passes an audit far more easily.
| Your problem | What we would recommend |
|---|---|
| Answering questions over documents | RAG with hybrid retrieval and citations |
| Predicting churn, demand or risk | Classical ML on your tabular data |
| Multi-step work across your tools | An agent with approval gates |
| Extracting fields from fixed forms | Document AI, not an LLM call per page |
| Deterministic rules and thresholds | Plain software, no model at all |
| Domain tone and vocabulary at scale | Fine-tuning with held-out evaluation |
Every scoping call ends with a written recommendation, including the option of not building anything.
Every number on this site is traceable to a system
We have a standing rule: no figure appears in a proposal, a deck or on this website unless it came out of a deployed system with a client attached. Each one below carries the engagement it was measured in.
Citation accuracy on legal queries, with zero hallucinated citations after DPO alignment
measured · legal-ai · prodSupport queries resolved without human escalation across seven channels
measured · support-platform · prodQualified pipeline generated in the first six months by the autonomous revenue layer
measured · revenue-platform · prodAccuracy classifying regulatory compliance gaps across eight jurisdictions
measured · regtech · prodReduction in debugging and incident resolution time inside an existing DevOps toolchain
measured · devops-platform · prodData residency inside the client VPC on the private deployment, with no external API calls
measured · private-llm · prodThree systems, three sets of numbers
Every figure below comes from a live production system with a real client. Full write-ups, architecture notes and client quotes are on the case studies page.
91% citation accuracy after fine-tuning Gemma 3 27B
A law firm running general-purpose models was getting hallucinated citations across 11 jurisdictions. QLoRA plus DPO alignment on 400,000 of their own documents removed them, at 60% lower inference cost.
Healthcare · USA27% faster patient intake under a HIPAA architecture
Patient records, lab data and medical literature unified into one queryable layer on GCP Vertex AI, with role-based access, a full audit trail and 91% factual accuracy in clinical responses.
Enterprise SaaS · USA and UK$340K of pipeline generated in the first six months
Fourteen disconnected sales tools replaced by one autonomous revenue layer on n8n and LangGraph, running 900 plus automated triggers a day with 48% less manual sales operations work.
Seven delivery stages. We own all of them.
Most AI engagements cover the middle three. Clients find out at handover that evaluation, deployment and operation belonged to somebody who was never hired. That gap is where AI projects die, so we close it by holding the whole line.
A delivery process built for production, not demos
Every engagement follows the same disciplined path. You see real output early, and you always know where the project stands.
Scope and KPIs
A free call to understand the workflow, the data, and the constraints. We define what success looks like in measurable terms before anyone writes code.
Architecture and Plan
A documented system design, model selection, cost model, and phased delivery plan. You approve the approach and the budget before we start building.
Build and Demo
Agile sprints with a working demo from sprint two onward. Weekly updates, honest reporting on blockers, and evaluation against the KPIs we set.
Deploy and Hand Off
Cloud deployment, monitoring, full documentation, and a clean handoff. Then we stay on for support, model tuning, and quarterly improvements.
Your customer decides what the system has to survive
A copilot serving fifty enterprise accounts and a support system serving two million consumers share a technology stack and almost nothing else. We scope against the constraint that dominates your model, then against your sector's regulation.
Low volume, high stakes per answer
Copilots, revenue intelligence and multi-tenant LLM features. The work is accuracy, permissions and tenant isolation rather than scale.
Consumer and B2CEnormous volume, thin margin per interaction
Autonomous support, real-time personalisation and forecasting, where cost per request and latency matter more than benchmark scores.
Marketplaces and platformsTwo-sided, and adversarial
Matching, ranking, fraud and trust-and-safety. Someone is actively trying to defeat the system, which changes the engineering.
Internal operationsMeasured in hours removed
Knowledge search, document processing and compliance automation, where the hardest constraint is usually undocumented legacy data.
What clients say after we deliver
Direct quotes from the people we built for. Every testimonial is tied to a real delivery.
“The results were immediate and measurable.”
Their revenue platform gave us the pipeline intelligence we had spent two years trying to build internally. I would recommend them for any serious enterprise AI initiative.
“A level we expect from top-tier enterprise vendors.”
Factual accuracy, HIPAA compliance and real-time performance had a direct, measurable effect on patient care quality and throughput across our network.
“Accountable well past the delivery date.”
The systems are solid, the documentation is thorough. That mix of technical depth and post-launch ownership is rare at this level of AI engineering.
Your AI system should work, not just demo well
Book a free 30-minute call. We listen to the use case, ask the technical questions that matter, and give you an honest read on what is realistic, how long it takes and what it costs.