Working AI inside
your operation.
Not strategy decks and not a chatbot on your marketing site. Agents and automation
that run against production systems, built by the same engineers who own the infrastructure
underneath them. As an Anthropic partner we build with Claude and Claude Code.
The approval gate is the architecture.
Most AI delivery fails one of two ways: it stays a demo, or it is handed autonomy
nobody signed off on. This is the loop we run in regulated environments, and the gate is a
structural component rather than a promise in a slide.
// A finding either becomes a reviewed pull request or a documented exception.
// There is no third path where something changes because a model decided it should.
92 findings, one day, six left open on purpose.
A HIPAA-regulated client was carrying 92 open security findings. It had
sat for months, and not because nobody cared. Remediating that volume by hand is weeks of
senior engineer time, and in a regulated environment every change needs an audit trail, so
the work is slower than the fix itself suggests.
We ran remediation through AI-assisted delivery with an engineer approving every single
change. The interesting part is not the speed. It is the six we did not fix.
Those six were closed as documented exceptions with written rationale,
because remediating them would have broken a dependent workload. A green dashboard with
those six force-fixed would have looked better and been worse. An auditor can tell the
difference, and so can the next engineer who inherits the environment.
member-facing
no exceptions
Four things, built to be owned by your team.
Every engagement ends with a handover. If your engineers cannot operate it on
day 31 without us, we have not finished.
Agents scoped to one workflow
Tool integrations, approval gates,
an evaluation harness and a runbook. Scoped to a single high-toil workflow rather than a
platform nobody asked for. Your engineers own it at handover.
Retrieval over your own systems
RAG and enterprise search with
permissions preserved end to end, so a query returns what that user is allowed to see and
nothing else, with citations back to the source document.
AI-assisted delivery
The loop above, applied to remediation,
migrations and infrastructure automation. Changes arrive as pull requests an engineer reads
before merging.
Governance and the platform under it
Which AI tools are actually
in use, policy at the prompt layer, model gateway, prompt and response logging, evaluation
and per-team cost attribution.
What we build on.
Anthropic Claude and Claude Code. AWS Bedrock
with VPC endpoints and IAM guardrails. MCP integrations where a tool boundary makes sense.
Vector search with permission-trimmed retrieval,
citation preservation and evaluation sets that catch regressions before a user does.
Approval gates, prompt and response logging, audit trails,
per-team cost attribution and no data egress outside the VPC.
What we will not do
We do not ship agents with unsupervised write access to production, and we will not
represent a pilot as a production system. If the honest answer to a workflow is that it does
not need AI, that is the answer you will get. Most of the value in this work comes from
removing toil, not from the model being clever.
Pick one workflow. We will make it real.
The one eating the most engineering time is usually the right place to start.
Bring it to a call with an engineer, not a salesperson.