Agentic AI that still works in month six

They fail at everything around it. If any of this sounds familiar, we have probably been called in to fix it.

• The pilot demoed beautifully and never shipped

  It worked on a curated dataset in a controlled room. Then it met real users, real edge cases and a real security review.

• The model is fine. The data underneath it is not

  Retrieval quality is a data problem, not a prompt problem. Without a foundation, you are tuning prompts forever. That foundation is its own discipline.

• Nobody can say what happens when the agent is wrong

  No evaluation set, no guardrails, no logs. So nobody will sign off on putting it in front of a customer.

• The vendor delivered and disappeared

  The system runs until the day something changes, and then no one on your side knows how to change it.

• Agent design and orchestration

  Single agents and multi-agent systems: what each agent is allowed to do, how they hand work to each other, and where a human stays in the loop.

• Retrieval and RAG

  Chunking, embedding, indexing and re-ranking your own content so answers are grounded in your documents rather than the model’s imagination.

• Vertex AI and Gemini Enterprise

  We build on Google Cloud’s agent stack — Vertex AI Agent Builder, Gemini models, and the surrounding services for storage, search and access control.

• Integration with the systems you already run

  Function calling into your ERP, CRM, ticketing or internal APIs, so the agent can do something rather than just describe it.

• Evaluation and guardrails

  An evaluation set built from your real cases, scored before and after every change. Refusal rules, escalation paths and audit logs for the answers that matter.

• Production and handover

  CI/CD, monitoring, cost controls and a runbook — plus training so your engineers own the system, not us.

1. 01

   Discovery and use-case selection

   We interview the people who would use the system, map the workflow as it exists today, and rank candidate use cases by value against feasibility.

   • A ranked use-case list — including the ones not worth building yet
   • A target architecture sketch
2. 02

   Data and retrieval foundation

   We assess the sources the agent needs, fix what makes them unusable, and stand up the retrieval layer with access control from day one.

   • A data and retrieval design
   • An ingestion pipeline
   • A first retrieval quality benchmark
3. 03

   Agent build and evaluation

   We build the agent against a real evaluation set, iterate on grounding and tool use, and run it with your team on real cases.

   • The working agent
   • The evaluation set and its scores
   • Guardrail rules and user-testing results
4. 04

   Production and handover

   Deployment, monitoring, cost and quota controls, and structured handover to your engineers.

   • Production deployment and dashboards
   • A runbook
   • A working session with the team that will own it

Scope, sequence and timeline are agreed with you before anything starts. Talk to our team and we will put dates against these four phases for your case.

• Semantic search at retail scale

  For a national retailer’s catalogue of hundreds of thousands of titles, we built generative semantic search — customers describe what they want instead of guessing the exact name. Client name and figures will appear once approved.

• Our own platforms

  We run AI in production on our own products, not only for clients. ShopSCAPE and Pantip MALL are where we take the first hit when something does not hold up at scale.