Most enterprise AI dies in the security questionnaire, not the demo. We build autonomous systems for Fortune 500 engineering, data, and revenue organizations that execute inside your own VPC, stop at a human approval before writing anything, and trace every claim back to the tool call that produced it.
Agent proposes updating 12 opportunity records. Diff preview attached.
[1] citation resolves to the exact tool call and result set
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Automated tests across shipped platforms
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Bytes of customer data leaving the customer VPC
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Tables an agent navigates without bulk schema injection
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Upper bound on a governed dashboard build
The pattern
The demo is never the hard part. The hard part is the eighteen months between a convincing prototype and a system your security, compliance, and platform teams will actually sign. Three failure modes account for nearly all of it.
Your prototype called a vendor API from a laptop. Production means regulated data, a data residency clause, and a security team that will not approve an architecture where proprietary information crosses your perimeter to be inferenced on someone else's hardware.
A model produces a confident number. No one can say which query produced it, whether the join was correct, or whether the model invented it outright. In a regulated forecast or a safety case, an unverifiable answer is worth less than no answer at all.
Read-only assistants are safe and largely worthless. The value is in the write: filing the test, updating the record, publishing the dashboard. That requires an approval gate, a blast-radius preview, and replay protection — engineering most pilots never budget for.
We treat all three as architecture problems, not prompt problems. That is the entire difference between a system that ships and a system that demos.
Solutions
Each one started as a Fortune 500 problem with a real budget and a real deadline. Each is running in production today. We deploy them as the starting architecture for your engagement rather than beginning from an empty repository.
A 500-page specification becomes reviewed, traceable tests in your ALM tool. Source repositories generate coverage directly. Thousands of inconsistent legacy tests get retrofitted to a golden structure in place.
Autonomous multi-step agents that qualify accounts, run competitive analysis, price deals, and update the CRM — inside a zero-trust execution environment your security team can actually inspect.
One sentence and a governed data source produce a real Power BI artifact — tables, relationships, DAX measures, visuals, page layouts — that opens directly in Desktop. Not a screenshot. A file your BI team owns.
Qualifying accounts discovered from public records, researched against real data, contacts resolved and verified, outreach drafted — then it stops. The draft lands in an outbox and a person decides whether it goes.
Shared architecture
Test engineering, revenue operations, and business intelligence look nothing alike from the outside. Underneath, they are the same control problem: let a model reason freely, then constrain what it is permitted to do with the conclusion.
Full security architectureYou cannot paste a 50,000-table catalog into a context window. The agent discovers what is relevant, previews it, counts it, and samples it — the way an analyst would — before it commits to a query. Large result sets are materialized to object storage and passed as a pointer, so the model never sees the raw rows.
The model decides what the dashboard should contain or which signals a requirement implicates. Deterministic code turns that decision into the actual artifact — TMDL, PBIR, a structured test. A validation loop catches malformed output and repairs it before anyone sees it.
Every capability is a separate package with its own store and telemetry, behind its own kill switch. A failing subsystem degrades to empty rather than taking a sibling down, and disabling one leaves the core pipeline byte-for-byte unchanged. That property is what makes staged rollout possible.
Reads run freely. Writes stop, every time, with a diff of exactly what would change and how far it reaches. Approval is pinned to a version token, so an approval granted against one plan cannot be replayed against a different one. Approve, reject, or escalate — and the decision is recorded.
Engagement model
We are not staffing a seat. We deliver a running system, the source, the infrastructure code, the decision records, and the test suite — then train your team to own it.
We meet your security team before we write code, not after. Deployment topology, data residency, identity provider, egress policy, and audit requirements are settled up front and written into the design. The output is a threat model and a deployment diagram your reviewers have already seen.
Synthetic data hides every problem that matters. We work against your real schemas, your real document corpus, and your real naming conventions from the first sprint, inside your environment. You see a working increment every two weeks, and every increment is deployable.
Red-team the agent against prompt injection, tool-shadowing, and egress evasion. Wire metrics, audit events, and alerting. Load the automated test suite into your CI. The exit criterion is not a demo that impressed someone — it is a green pipeline and a runbook.
Full source transfer, infrastructure-as-code, architecture decision records, and hands-on enablement for the engineers who will carry it. If we vanished the following Monday, the system would keep running and your team would be able to extend it. That is the standard we hold ourselves to.
Build the business case
Enter your headcount, loaded cost, and current cycle times. The calculator runs entirely in your browser and shows reclaimed engineering capacity, annualized cost recovery, and payback period against a realistic deployment. Nothing is transmitted while you model.
Open the ROI CalculatorIllustrative figures for a 40-engineer verification group. Your inputs will differ.
Questions we get first
Direct answers. If yours is not here, ask it in the briefing.
No. The execution plane runs inside your own VPC under a bring-your-own-cloud model. Agent-generated code executes in hardware-isolated MicroVMs on your infrastructure, and an eBPF egress filter plus DNS sinkhole restricts outbound traffic to an allowlist you control. The control plane orchestrates; it never holds your data.
No. Every WRITE action halts at a human-in-the-loop gate that renders a blast-radius diff before anything commits. Approvals are pinned to an optimistic-concurrency version token, so an approval granted against one plan cannot be replayed against a modified one. Reviewers approve, reject, or escalate, and every decision is written to the audit log.
Every claim carries a citation the backend generated, not the model. Each marker resolves to the exact tool call and result set behind it, so a reviewer can open the underlying query. Markers the model invents on its own are stripped before rendering, and citations survive across conversation turns rather than resetting each message.
Six to twelve weeks for a scoped pilot. Weeks one and two cover architecture and security review, weeks three to eight build against your real data inside your environment, and the remainder covers hardening, red-teaming, runbooks, and handover. We start from three shipped platforms rather than an empty repository, which is where the compression comes from.
Yes — that is the deliverable. Engagements terminate in full source transfer, infrastructure-as-code, architecture decision records, and the automated test suite, plus hands-on enablement for your engineers. You are not renting a black box, and there is no extraction cost if you decide to take it entirely in-house.
Forty-five minutes with the engineers who built these systems — not a sales team. Bring the data residency clause, the audit requirement, or the legacy system everyone says cannot be integrated. You will leave with an architecture opinion whether or not you work with us.