Siloed
Four systems, four schemas
Job numbers differ between accounting and field software. Customers are spelled three ways. Nothing joins cleanly.
Services // AI architecture
AI data architecture and systems integration: the layer on top of your software that you own.
For companies whose AI projects keep failing at the same place: the data was never ready for a machine to read.
Before any agent or dashboard works, your operation has to be machine-readable. We design and build that layer: connectors into each system, one database that holds structured records and their embeddings side by side, and a model router that sends each task to the model that fits it. The model is the last piece, not the hard one.
Postgres with pgvector, SQL, scheduled and API-based connectors, Power BI and QuickSight on top, Claude and other models routed per task.
The problem
Why the model was never the problem.
Every failed AI project we have been called into failed for the same reason: the business was not machine-readable. The fix is architecture, not a better prompt.
Stale
Nobody knows how fresh the data is
Some platforms give a live API. Some give a nightly export. If you do not know which, your agent confidently answers with yesterday.
Text is invisible
Contracts and notes are not queryable
Structured tables miss the story in the PDFs, emails, and field notes. Without embeddings, that knowledge stays locked.
Wrong model, wrong job
One model for everything
A long-context reasoning model on high-volume drafting burns budget. A lean model on a 200-page contract misses the clause. Routing matters.
What you get
What the architecture looks like.
One database, clear connectors, a model router, and ownership that stays with you.
01 / Connectors
Into every system you run
APIs where the platform offers one, scheduled exports where it does not, with data freshness recorded and visible. Read-only by default.
02 / One database
Records and their vectors, side by side
Postgres with the pgvector extension holds relational tables and embeddings in the same store. That is what makes a hybrid query possible: filter by job and date, then search the contract text, in one pass.
03 / Model router
Right model per task
An embedding model turns text into vectors. A long-context model reads documents. A leaner model handles volume. Each task goes to the model that fits it.
04 / Governance
Permissions, logging, handover
Access scoped to the user's own system login, every agent action logged, documentation you can hand a future hire. You own the whole stack.
How it works
How we design it.
Architecture work starts inside the Automation Blueprint and is built in Phase 2.
01
Inventory and access map
Every system, what it exports, how fresh, who owns the login, and what it will and will not let us write.
02
Design the data layer
Entity model for jobs, cost codes, customers, and crews. Which text gets embedded. Which model handles which task. Written down and priced.
03
Build, load, validate
Connectors run, the database fills, and we reconcile against your books before a single dashboard or agent reads from it.
What the work finds
Numbers from real builds.
Every Omadli dashboard and agent runs on this layer. It is why a dashboard averages about 18 days to go live and why agents such as Farmon can answer from your own documents.
Questions operators ask
AI data architecture questions, answered.
Neither, strictly. It is a single operational database, Postgres with the pgvector extension, that holds your relational records and the vector embeddings of your text in the same store. A data lake is raw files in object storage; that is not what an operating company of your size needs.
No. An embedding model turns text into vectors. pgvector stores those vectors and runs the similarity search. Keeping vectors next to the structured rows is what allows a single query to filter on job data and search contract text at once.
Sage Intacct and Sage 100/300 CRE, Trimble, BuildOps, Procore, Autodesk, Aspire, JobTread, QuickBooks, Salesforce, HubSpot, Microsoft 365, isolved, Yardi, monday.com, ServiceTitan, Housecall Pro, Jobber, GoHighLevel, Google Drive, SharePoint, Laserfiche, and Knowify. Each connects the way that platform allows; we tell you the data latency for each one.
Where the platform allows it and you approve it. Write access is open on some systems, partner-gated on others, and closed on a few. The layer stages the action either way, so where a platform is closed you still get the ready entry to post.
Yes. The database, the connectors, the models, and the documentation are yours. We can host and maintain it under the Operations Partnership, or hand it to your IT team.
Yes. We are based in Boston and regularly work alongside a client's existing IT provider. We handle the data and AI layer; they keep handling the network and devices.
Boston, Massachusetts
Local to Greater Boston, working nationwide.
Omadli Group is based in Boston, MA and is a Massachusetts certified Woman-Owned Business. We meet Greater Boston clients in person, run Claude Training Labs in Watertown, and build remotely for operators across the United States.
People find this service by searching for
- AI architect Boston
- AI data architecture consultant
- systems integration for construction software
- connect Sage and BuildOps
- data warehouse for small business
- AI-ready data infrastructure
Related
Start with the Blueprint.
Assessment, roadmap, costing, and quick wins, run on your own data. From $5,000 per scope.