Agentic systems that do real work.
Agents, RAG and LLM orchestration — engineered with the same discipline as the platforms underneath them.
The demo took a week. Production has taken a year, and it still isn't there. Answers can't be trusted, costs can't be predicted, and nobody can say what the system will do when the input changes. Most agentic projects fail the same way: impressive prototype, no engineering underneath.
- Agent architectures single and multi-agent systems with clear task boundaries, revision loops, and human review points where they belong. Built on Google's ADK or LangGraph; we've implemented the same production pipeline in both and will tell you honestly which fits your case.
- RAG on your knowledge retrieval over governed enterprise data: chunking and embedding strategy, vector store design, citation-backed answers. Not a wrapper around a search box.
- LLM orchestration vendor-agnostic model access, routing between models by cost and capability, structured outputs, budget hard-stops.
- MCP integration your systems exposed as tools that agents (yours or your customers’) can safely use. Read-only by default, permissioned by design.
- Evaluation and grounding a golden-set harness before launch, a grounding check on every output: no number leaves the system that wasn't in the input. This is the part most builds skip. It's the part that decides whether yours survives.
- Weeks 1–2 — Discovery We map the workflow, the data it needs, and the failure modes that matter. Output: an architecture you own, whether or not we build it.
- Weeks 3–8 — Build Working system in your cloud, evaluated against an agreed golden set, with cost and latency measured — not estimated.
- Handover Your engineers run it. Documentation, runbooks, and the evaluation harness stay with you.
We contributed to Vodafone's enterprise GenAI platform — vendor-agnostic LLM endpoints, vector stores and knowledge bases behind a central orchestration gateway. The same discipline shapes Urekaa.ai, our stock-analysis product: an agentic news pipeline, with a critic agent that rejects any claim it can't trace to a source, currently in pre-launch.
Google ADK · LangChain / LangGraph · Vertex AI · Gemini / Claude / open models · MCP · vector databases · Pub/Sub · Cloud Run
Bring us the workflow. We'll tell you in two weeks whether agents can do it — and prove it in eight.
Talk to us