Process

Five phases. No surprises at security review.

Security, cost and evaluation are designed in from the first week, not bolted on before launch. You see working software against real data every week.

  1. 01

    Discover

    1–2 weeks

    We map candidate workflows, data sources, risk constraints and success metrics, then pick the use case with the best ratio of value to risk.

    Deliverable:Prioritized use-case map & risk register
  2. 02

    Blueprint

    1–2 weeks

    Agent topology, tool boundaries, identities and approval points, designed before any code is written, and reviewed with your security team.

    Deliverable:Architecture & threat model
  3. 03

    Build

    4–8 weeks

    We build in your cloud with infrastructure-as-code, tests and evals from day one. Weekly demos run against real data, not slides.

    Deliverable:Working agent system in your environment
  4. 04

    Harden

    1–2 weeks

    Red-team the system, tune thresholds against eval sets, and wire up tracing, cost caps and CI invariants.

    Deliverable:Red-team report & go-live checklist
  5. 05

    Operate

    Ongoing

    Hand-over to your team, or managed LLMOps. Either way, the same dashboards, runbooks and budget guardrails come with it.

    Deliverable:Runbooks, dashboards & handover

Engagement models

Start small. Scale what works.

AI Readiness Sprint

2 weeks

For teams deciding where GenAI fits. We assess your data, workflows and risk posture and deliver a prioritized roadmap with an architecture sketch for the top use case.

  • —Use-case prioritization
  • —Data & security assessment
  • —Reference architecture
Most popular

Agent Pilot

6–10 weeks

For one high-value workflow. We design, build and harden a production-grade agent system in your cloud, starting from one of our suites or from scratch.

  • —Production-grade build
  • —Evals & red-team report
  • —Go-live in your environment

Scale & Operate

Ongoing

For organizations running several agents. Shared platform, approval UI, observability, and continuous red-teaming as you add use cases.

  • —Agent platform & governance
  • —Managed LLMOps
  • —Continuous red-teaming

FAQ

Questions we hear from security and finance.

Does our data leave our environment?+

We build in your cloud account, under your IAM, with your model endpoints. Data is accessed by scoped identities at call time, and we don't take copies to our own infrastructure.

Which models and clouds do you work with?+

We're model-agnostic. Much of our recent work runs on Google Cloud (Vertex AI, Agent Engine, ADK), and the same architecture patterns apply on AWS and Azure. We pick models per task: a cheaper tier where the work is routing, a stronger tier where it's generative.

How do you stop an agent from doing something it shouldn't?+

Structurally, not with prompts. Agents only get the tools they need, each tool runs under its own least-privilege identity, and every external action is halted by a platform-level approval gate until a person signs off.

We're mid-size, not a Fortune 500. Is this overkill?+

No. The guardrails are what let a small team trust an agent enough to use it. Our Readiness Sprint and Pilot are scoped to deliver one workflow well rather than a sprawling program.

Can your suites be customized to our business?+

Yes. The CMO, CFO and Red-Team suites are starting points. Specialists, data connections and approval rules are adapted to your systems and policies during the Blueprint phase.

Ready to put an agent into production?

Tell us about the workflow. We'll tell you honestly whether an agent is the right answer, and what it would take to make it safe.

Book a Strategy CallSee Agent Suites