Proof · Four engagements

What we have already built

Each of these is drawn from systems our team built and operates. The figures are the ones we can show you in the code.

01 · Modernization and DevSecOps

Off one server, onto Kubernetes, without a rewrite

Helm charts

203

CI workflows

Challenge

Nine Java services and an Angular front end served live users from a single hand-deployed EC2 instance. Java and Maven were installed on the box by configuration management, each service was cloned and built there, and processes ran under nohup behind NGINX. One node, one failure domain, manual releases, no rollback, no environment isolation.

Approach

We changed the substrate and left the applications alone. Multi-stage containers that discard the build toolchain. A declarative cluster definition across three availability zones with control-plane logging and encrypted cluster secrets. Stateful resources, eight databases, fifteen registries, four buckets, CDN, search domain and shared filesystem, in CloudFormation, parameterized so production and non-production are the same template. Each service as a Helm chart with autoscaling, rolling updates and three health probes. Every secret moved to parameter store.

Result

The replacement was proven before the predecessor was abandoned, with three days of overlap between the new stack's first commit and the old stack's last. It ran four and a half more years, growing to 25 Helm charts and 203 CI workflows without another substrate change. Single-node manual deployment became zero-downtime rolling updates with per-service autoscaling and centralized structured logging. No application source was rewritten to get there.

02 · Software factory

Every pull request gets a full copy of production

Challenge

On a ten-service, eight-database platform, a change in one service breaks another in ways unit tests never see, and schema changes break both. Shared staging environments serialize the team and drift from production.

Approach

We built environment provisioning into the pull-request lifecycle. On open, the workflow builds the image, copies all eight databases from a template into a request-scoped set, applies the request's own schema change to those copies, deploys the entire platform into a fresh namespace with only the changed service overridden, and comments the URL back. Sync workflows keep it current; a teardown workflow destroys it and drops the databases on close. In parallel, four required checks run on every push: tests, style, format and dependency scanning.

Result

Hundreds of pull requests across seven repositories each got a private, database-complete replica of the whole platform, automatically, with no ticket and no queue, reclaimed automatically so cost did not accumulate. Supply-chain scanning applied at the same gate. The pipeline, not the reviewer, decided what was mergeable.

03 · Data migration

Retiring a system of record without losing a row

27,987

records reconciled

99.79%

lead records matched

3,030

flagged for a person to decide

Challenge

A commercial CRM plus an accounting system held a decade of customer, enrollment and payment history. The replacement had to carry all of it, and the money trail had to survive the move intact.

Approach

We wrote the comparison before the migration. A read-only consolidation script parsed the exports, derived the cohort structure, matched exactly on email and then conservatively on name, and emitted files for human review rather than writing anything. Unmatched and ambiguous records were surfaced, not absorbed. The migration itself ran with idempotency guards and printed its own verification.

Result

27,987 records reconciled: 24,544 matched by email, 310 by name, 103 new, and 3,030 deliberately flagged for a person to decide. Separately, 76,401 of 76,562 lead records matched, 99.79%. The live enrollment run moved 20,299 records to 20,772, deduplicated 13 programs to 7 with zero orphaned foreign keys, correctly skipped 35,683 records that should not move, and reported zero unmapped values.

04 · AI implementation

AI extraction a reviewer can audit

5,574

contracts extracted

96.7%

blind re-extraction of 150 agreeing

$84.2M

audited across a decade

Challenge

Ten years of contracts, invoices and payments across three processors had to be reconciled against signed agreements, and every dollar had to be defensible to someone who did not trust the model.

Approach

Extraction is allowed, but not on its own authority. Every field the model returns must carry an excerpt; code then checks that the excerpt appears literally in the source document and discards the field if it does not, recording the page. Name matching auto-accepts only above 0.92 similarity, and the 0.80 to 0.92 band always routes to a human, because a dry run showed that band full of wrong-person matches. Model-suggested amounts are clamped to configured floors. Outbound messages sit in an approval queue and only approved ones send.

Result

5,574 contracts extracted, with a blind re-extraction of 150 agreeing 96.7%. $936,931 reconciled across 874 accounts, 81.1% closed in full. Separately, a three-rail reconciliation audited $84.2M across a decade and lifted the share of payments attributable to a named individual from 7.25% to 39.19%, with zero false-positive name matches and the unmatched residual published rather than buried.

Verification

Ask us for the artifact behind any number here

The traceability matrix, the migration report, the security audit and the remediation migrations are all real files. We will walk you through them.

Request a walkthrough