Generative AI & Agents
AI agents that do real work, safely.
We design, evaluate and run generative AI systems on top of your governed data: enterprise search, copilots and agents that complete multi-step tasks with clear guardrails and audit trails.
Sound familiar?
The problems we solve
Impressive demos, unreliable products
Prototypes that work in a meeting and fail on real documents and edge cases.
Hallucinations and trust
Answers that sound right but cannot be traced to a source.
Security and data leakage
Concerns about sensitive data reaching external models or the wrong users.
Runaway costs
Token spend that grows faster than usage without monitoring or caching.
Outcomes
What you get
- Assistants grounded in your documents and data, with citations
- Agents that automate multi-step workflows with human approval
- Measured quality through systematic evaluation, not demos
- Security, privacy and cost controls from the first release
Approach
How we deliver it
- 1
Pick the right workflow
Score candidate use cases on value, feasibility and risk.
- 2
Ground in your data
Document processing, retrieval and access control that respects permissions.
- 3
Evaluate relentlessly
Test sets, automated scoring and human review before and after launch.
- 4
Operate safely
Guardrails, audit logs, cost monitoring and feedback loops in production.
Deliverables
What we hand over
Working software, tests and documentation in your repositories and cloud accounts.
Technologies
- Claude
- Azure OpenAI
- Amazon Bedrock
- Vertex AI
- LangGraph
- Vector databases
- Model Context Protocol
- Use-case discovery
- Shortlist and value case for the workflows where generative AI pays off.
- Retrieval (RAG)
- Document processing, embeddings and retrieval tuned for accuracy.
- Agentic workflows
- Tool-using agents integrated with your business systems, with approval steps.
- Evaluation and guardrails
- Test sets, automated evaluation, red-teaming and policy enforcement.
- LLMOps
- Prompt and model versioning, observability and cost monitoring in production.
Use cases
Where this makes a difference
Enterprise knowledge assistant
Answers from policies, contracts and manuals, with citations and permission-aware retrieval.
Document processing agent
Invoices, claims or applications extracted, validated and routed with human review.
Analyst copilot
Natural-language questions answered from governed metrics, with the SQL shown.
Customer service agent
Drafted replies and resolved tickets with escalation to people when needed.
FAQ
Common questions
Will our data be used to train public models?
No. We deploy on enterprise endpoints with data-retention controls, inside your cloud where required.
How do you measure quality?
With task-specific test sets, automated evaluation and human review, tracked over time like any other software metric.
Which model do you use?
The one that fits the task, cost and data-residency needs. Systems are built so models can be swapped as the market moves.
Related services
All services →Machine Learning & MLOps
Forecasting, scoring and optimization models taken from notebook to production, with monitoring, retraining and governance.
Models from notebook to production
AI Strategy & Advisory
A practical roadmap from AI ambition to delivered value: use-case prioritization, data readiness, operating model and responsible AI.
Roadmaps, readiness and responsible AI
Data Engineering
Reliable batch and streaming pipelines that move data from every source into your platform, tested and observable from day one.
Batch and streaming pipelines you can trust
Tell us where your data is today.
A 45-minute conversation with a senior engineer. You leave with an honest view of what to build first, whether or not we work together.