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Legacy System Integration

Don't migrate. Bridge.

We make your old, clunky software talk to modern AI so you don't have to migrate your entire business. APIs, scrapers, and middleware that bridge what you have to what you need.

The case for bridging

Your old system isn't the problem.

It still runs your business. It holds your data. Your team knows it. Migrating means months of pain, retraining, and a long tail of edge cases nobody documented.

We don't ask you to migrate. We wrap a modern layer around what you have. Your old system keeps doing what it does. The new layer feeds AI, exposes APIs, and makes everything talk.

$ read legacy_db.dbf
→ 14,820 records loaded
$ normalize → postgres
→ schema mapped, 0 errors
$ expose REST
→ /api/v1 live
$ connect agent
→ agent reading + writing
$
What we bridge

The systems nobody wants to touch.

Legacy ERPs

JD Edwards, NetSuite (the old kind), SAP, Sage. We read, write, and surface what's locked inside.

On-prem databases

SQL Server, Oracle, DB2, even FoxPro. Mirrored to a modern store, queryable by AI.

Spreadsheet operations

The Excel that runs the company. Pulled into a real database without breaking what people use.

Industry software

Trade-specific tools with no API. Playwright drives the UI. Data flows in and out.

Email-based workflows

PDFs in inboxes, attachments parsed, structured data extracted, fed downstream.

Print + paper

Scanned forms, OCR'd, validated, normalized. The last mile of paper-to-digital.

How it works

Three layers. No rip-and-replace.

Layer 1 · Read

Get data out

Direct database connection where possible. Scrapers and OCR where it isn't. Whatever it takes to mirror the source of truth into a modern store.

Layer 2 · Expose

Modern API

REST or GraphQL on top of the mirror. Authentication, rate limits, audit log. Now your data is callable by anything modern.

Layer 3 · Write

Push back safely

When AI or new tools need to write, we route through validation, conflict checks, and transaction safety. Your old system stays trustworthy.

Operate

Monitor and evolve

Schema drift alerts. Sync health dashboards. Optional monthly support. Eventual migration path if you ever want it, but never required.

Real examples

What this looks like in practice.

Manufacturing

FoxPro to AI in 3 weeks

Inventory ran on a 1998 FoxPro database. We mirrored it to Postgres nightly, exposed REST, and pointed an AI agent at it for natural-language queries.

Healthcare

Faxes into structured data

Referrals came in as faxes. We OCR'd, parsed with an LLM, validated against the EHR schema, pushed structured records. Saved 4 FTE.

Construction

Wrapping a 20-year-old PM tool

No API, no docs. Playwright automation read every project, mirrored to Postgres, exposed an API, and let modern dashboards live alongside the old UI.

Engagement

One bridge. Built right.

Integration Build

One legacy system, fully bridged

Discovery, mirror, expose, write-path, monitoring. Your old stack keeps running. The new layer makes it modern.

Custom scoped to system

  • Discovery against the actual system
  • Mirror layer (DB, scraper, or OCR)
  • Modern API on top of the mirror
  • Safe write path with validation
  • Monitoring + drift alerts
  • Documentation + handover
  • Optional monthly support
Scope an Integration

Keep what works.
Add what's missing.

Migration is the worst-case option. Bridging is almost always the better one.

Start a Discovery