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Services

Systems for the work that repeats.

Four service lines, one shape: we take a process that consumes your team's hours every week, build an AI system around it, and hand it over documented. Each line below shows the architecture we build from — not a slide, the actual pattern.

Document intelligence

Invoices, contracts, intake forms, correspondence. Documents arrive in whatever format the sender chose, and someone on your team retypes what matters into your systems. That is the work we remove.

  • Ingestion for PDF, scans, and email attachments
  • LLM extraction into your schema, validated against business rules
  • Confidence thresholds: high-confidence records file automatically, the rest queue for a person
  • Full audit log of every extraction and correction
After handover, your team runs The review queue, the business rules, and the exception list. Adding a new document type is configuration, not a new project.
Documents pdf · scan · email Extraction LLM → your schema Validation rules + confidence Auto-file high confidence Human review everything else EVERY STEP LOGGED · CORRECTIONS FEED BACK INTO RULES
Fig. 01 — Document intelligence pipeline

Reporting & analysis automation

The monthly close pack, the weekly ops report, the board deck. Recurring reports follow the same steps every cycle: pull data, reconcile it, format it, write the summary. Machines do the first three well, and drafting the fourth is now viable — with an analyst deciding what ships.

  • Scheduled collection from your source systems
  • Automated reconciliation with flagged mismatches, not silent fixes
  • LLM-drafted narrative summary against the assembled numbers
  • Analyst review step before anything is distributed
After handover, your team runs The schedule, the source connections, and the report templates. Your analysts spend the cycle on review and judgment, not collection.
ERP / Finance CRM / Ops Sheets / DB Collect & reconcile Draft report numbers + narrative Analyst review approve → send MISMATCHES FLAGGED, NEVER SILENTLY FIXED · NOTHING SHIPS WITHOUT APPROVAL
Fig. 02 — Reporting automation pipeline

Knowledge systems

Your policies, SOPs, contracts, and institutional memory live across drives, wikis, and inboxes. The people who know where things are spend their time being asked. We build retrieval systems over your own documents that answer with citations — so every answer can be checked against its source.

  • Indexing over your existing document stores, respecting existing permissions
  • Retrieval-augmented answers with source citations on every response
  • "Not in the documents" as a first-class answer — no guessing
  • Usage review so you can see what people ask and where documentation is missing
After handover, your team runs The document sources, the access rules, and the review dashboard. Content owners update documents; the system stays current.
Your documents policies · SOPs · wikis Index permission-aware Retrieve + answer grounded in passages Answer sources cited unanswered questions reveal documentation gaps "NOT IN THE DOCUMENTS" IS A VALID ANSWER · NO UNGROUNDED RESPONSES
Fig. 03 — Knowledge system architecture

Data & analytics QA automation

Our deepest specialty. Data-quality dashboards tell you something failed; a person still has to investigate, file the ticket, and chase the owning team. And verifying that analytics events fire correctly — from the client, through ingestion, to the semantic layer — is week-long engineering work per user journey. We build agents that do both.

  • Agents that read your test results, analyze failures against source tables, then file, prioritize, and route tickets to the owning team
  • Plain-language end-to-end test journeys: describe the interaction and the expected events, and the agent drives the client and verifies every backend layer
  • Results and audit-trail tables for every run — evidence, not just green checkmarks
  • Human review on ticket routing until the accuracy earns your trust
After handover, your team runs The test definitions, the routing rules, and the audit tables. New journeys are a sentence of instruction, not a sprint of engineering.
Test results dq · e2e runs Agent analysis finds root cause source tables queried Ticket filed prioritized · assigned Owning team auto-assigned EVERY RUN WRITES TO RESULTS + AUDIT TABLES · ROUTING REVIEWED UNTIL TRUSTED
Fig. 04 — Data & analytics QA pipeline
How an engagement runs

Working session

45 minutes · No charge

We map one process with you and give you an honest read on whether automation is worth it. If it isn't, we say so, and you keep the notes.

Process audit

Fixed scope · Fixed price

A structured review of the process: volumes, systems, failure points, and a concrete build recommendation. It produces a written audit document — here is a sample so you know exactly what you're buying.

Build & handover

Scoped in the audit · Ends with your team running it

We build in your environment, train your team as we go, and hand over the system with its documentation. The engagement ends when your team operates it without us.

Start with one process.

Bring the process to the working session. We will tell you what a build takes — or tell you not to build.

Book a working session