AI & Generative AI Engineering
Intelligent copilots, agentic workflows, RAG pipelines, and machine learning models built for enterprise automation.
From pilot to production.
Most organizations can already demo a chatbot. What stalls is everything after: grounding answers in real company data, proving the system is accurate enough to trust, controlling token spend, and passing a security review.
We engineer that part. Retrieval that cites its sources, evaluation harnesses that catch regressions before users do, guardrails that keep the system inside policy, and deployment on infrastructure your team already runs.
Because the same team runs our data engineering practice, we can fix the upstream data problems that limit model quality instead of working around them.
Typical engagements
- Internal knowledge assistant over policy and contract archives
- Customer support copilot with human handoff
- Document extraction replacing manual data entry
- Agentic automation of a multi-step back-office process
- AI readiness assessment and roadmap
What we build.
Enterprise copilots
Assistants grounded in your own documents, tickets and wikis, with source citations and role-based access.
RAG pipelines
Chunking, embedding, hybrid search and re-ranking tuned to your corpus rather than a default template.
Agentic workflows
Multi-step automations that call your systems, with approval gates and full audit trails.
Document & OCR intelligence
Structured extraction from PDFs, scans and forms, feeding straight into downstream systems.
Custom ML models
Forecasting, classification, recommendation and anomaly detection, trained on your historical data.
Evaluation & guardrails
Golden datasets, regression suites, prompt-injection defenses, PII redaction and cost ceilings.
Built on infrastructure you already trust.
We deploy inside your cloud tenancy and your security boundary, no data leaves your environment for a vendor we chose.
LangChain
LlamaIndexpgvectorPinecone
MLflow
Databricks
Python
FastAPI
Kubernetes
Three ways to start.
AI readiness assessment
Two to four weeks. We audit your data, identify the highest-value use cases, and deliver a costed roadmap.
Build and handover
Scoped delivery of a working system, with documentation and knowledge transfer to your team.
Embedded engineers
Senior AI engineers working inside your team and your sprint cadence for a fixed monthly capacity.
Common questions.
Will our data be used to train a model?
No. We deploy into your cloud tenancy using enterprise endpoints that exclude your prompts and documents from provider training. Data residency and retention are set out in the architecture document before the build starts.
How do you prove the system is accurate?
Every engagement includes an evaluation set built from real questions and verified answers. We report accuracy, citation quality and failure modes against that set at each sprint review, and the suite runs in CI so regressions surface immediately.
Our data is messy. Can we still start?
Usually yes, for a narrow use case. Where data quality is the binding constraint, the assessment will say so and recommend the data engineering work that has to come first rather than shipping something that quietly misleads users.
What does it cost to run?
We model token, hosting and retrieval costs during the architecture phase and build in caching, model routing and rate limits so monthly spend stays predictable.
Have a project in mind?
Tell us about your project and a member of our engineering team will get back to you within one business day.