AI that works on your data, and keeps working in production.
AI consulting and engineering: readiness assessments, LLM applications, RAG and agents grounded on your governed data, evaluated before they ship and monitored once they are live. Text, documents, voice and vision, not just chat.
- Agent Bricks
- MCP
- Vector Search
- MLflow 3.0
- Voice models
- Vision & video
- Model Serving
The market moved to agents. So did our practice.
Every agent gets the same treatment: grounded on governed data, tool access through MCP, an evaluation harness, and guardrails at the gateway.
Data agents
Ask governed data anything — the agent plans the structured query, executes through catalogs and gateways, and returns validated answers. Queryline, one of our Labs experiments, is an example.
Document agents
RAG done properly: parsing, chunking, Delta-synced Vector Search, hybrid retrieval, and answers with citations — over contracts, wikis, SharePoint, and archives.
Workflow agents
Tool-calling and multi-agent orchestration for real processes — triage, reconciliation, report generation — with human approval gates where they belong.
Voice agents
Real-time speech-to-text, reasoning, and TTS — call handling, field-ops voice interfaces, meeting intelligence.
Vision & video agents
Image and video understanding at pipeline scale — inspection photos, footage triage, frame-level extraction into structured tables your data platform already governs.
Embedded copilots
Assistants inside your own product — chat panes, inline suggestions, dynamic views — powered by the same governed retrieval and serving stack underneath.
Your data in, a governed agent out.
Evaluation isn’t a phase: the harness, LLM-judge scorers and tracing run from the first prototype to production monitoring. Guardrails and PII detection sit at the gateway, so every agent inherits governance.
Retrieval-grounded agent
- Your sources
- Ingest + prepare
- Vector Search
- Agent
- Evaluation
- Model Serving
- Your app
Readiness first, hype never.
- 01Step 1 of 4
Readiness assessment
Data quality, governance posture, and an agent-use-case shortlist ranked by value and feasibility, not by excitement.
- 02Step 2 of 4
Grounded prototype
A working prototype on your real data, usually within two to three weeks, with an evaluation dataset built alongside it.
- 03Step 3 of 4
Hardening
Guardrails and PII detection at the gateway, scoped MCP tool access, rate limits, provisioned-throughput decisions, tracing on every call.
- 04Step 4 of 4
Production & monitoring
Deployment on a data and serving platform we design, continuous evaluation, and a quality dashboard your team owns.
AI infrastructure that is secure and affordable.
Every AI system needs a home. We design it with the same care as the model work:
- Serving choices: managed model APIs, provisioned throughput or self-hosted models, chosen by cost, latency and data residency.
- An AI gateway: one place for routing, rate limits, guardrails, PII detection and audit logs.
- Private networking: model and vector endpoints reached over private links, not the open internet.
- Cost controls: per-feature budgets, caching, and dashboards that show spend per use case.
- Infrastructure as code for every component, so environments are repeatable.
Production Agent on Agent Bricks.
A fixed-scope build that takes one agent use case from your documents or tables to a monitored production endpoint.
- 01Source ingestion + chunking pipeline
- 02Delta-synced Vector Search index
- 03Agent with scoped MCP tool access
- 04Evaluation dataset + LLM-judge scorers
- 05Model Serving endpoint + guardrails
- 06Monitoring via MLflow traces
AI engineering questions.
What is RAG?
Retrieval-augmented generation: before a language model answers, the system retrieves the most relevant passages from your own documents or tables and gives them to the model, so the answer is grounded in your data and can cite its sources.
How do you know an AI system is good enough to launch?
We build an evaluation dataset with your team alongside the prototype, score every version against it with automated and LLM-judge scorers, and agree a quality bar before launch. The same scorers keep running in production.
Is our data ready for AI?
That is what the readiness assessment answers. We look at data quality, access control and governance, and tell you honestly which use cases your data supports today and what needs fixing first.
Where will our AI system run?
On cloud infrastructure we design with you on AWS or Azure: managed model APIs or self-hosted models behind a private gateway, with network isolation, guardrails, monitoring and cost controls, deployed as code.
Have an agent in mind?
Tell us what it should do. We will tell you honestly whether your data is ready for it.