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PRESTYJ

AI operations agents

AI operations agents for document processing, reconciliation, and back-office work.

Operations work is matching, checking, and filing across systems that were never designed to agree. We build agents that do the matching, send every mismatch to an exception queue with the reason, and leave postings to the people accountable for them.

Book a scoping call How an engagement works

Who this is for

  • Finance and operations teams that key data from documents into an ERP or accounting system
  • Back offices reconciling records between two or more systems every week or month
  • Operations leads who need a trail showing what was checked, by what, and who approved it

What we build

Examples of what can be built. These describe the kind of system, not case studies or client results.

  • Document processing

    Reads invoices, purchase orders, contracts, or forms, extracts the fields you specify, validates them against vendor and contract records, and queues anything that does not match.

  • Reconciliation

    Matches transactions or records across systems, explains each break with the evidence from both sides, and proposes the adjustment for a reviewer to accept or reject.

  • Recurring reporting

    Assembles the monthly or quarterly operating report from source systems, ties totals back to the ledger, and drafts the variance notes.

  • Exception queue management

    Triages incoming exceptions, gathers the context a person needs to decide, and tracks each one to resolution so nothing ages silently.

How an engagement works

Every engagement is scoped per workflow, source systems, volume, and control requirements, and priced in writing after scoping.

  1. Pick one workflow

    We start from one recurring piece of work: who does it today, what they open, what they produce, and who signs it off. A build covers that workflow, not a department.

  2. Map the source systems

    We list every system the work touches, how each one authenticates, what the agent may read, and what, if anything, it may write. Systems we cannot reach safely are named as out of scope.

  3. Size the volume and the controls

    Item counts, peak periods, and the review rules your team needs decide the design and the cost. You get a written scope that names the workflow, systems, volume, review owner, and exclusions.

  4. Build against real cases

    We build with test cases drawn from your own past work, including the awkward ones, and run the agent in review-only mode first so your team can compare its output to what they would have produced.

  5. Operate and maintain

    We host, monitor, and maintain what we build: source API changes, model updates, new exception types, and changes to your review rules. You see what ran, what was held, and why.

Controls built in

The same controls apply to every agent we run.

  • Least privilege

    Each agent gets its own credentials with the narrowest scope the workflow needs. Read-only is the default; every write path is named in the scope and switched on separately.

  • Review gates

    You decide which outputs a person approves before anything leaves the agent: a CRM update, a sent email, a posted journal entry, a published asset. Nothing that changes a record or reaches an outside party skips its gate.

  • Exception queue

    Items the agent cannot resolve with confidence, such as missing fields, conflicting sources, or unknown formats, go to a named queue with the reason attached instead of being guessed.

  • Audit log

    Every run records the inputs read, the tools called, the output produced, and who approved it, so any result can be traced back to its source.

  • The system of record stays authoritative

    Your CRM, ERP, or document store remains the source of truth. Agents read from it and propose changes to it; they do not keep a parallel copy that drifts.

Engineering we have shipped

Built and tested by Prestyj. Client names withheld.

  • Portfolio data connector for an investment firm

    Yardi via Power BI · Monday.com · SharePoint

    A remote, read-only MCP server that answers portfolio questions from evidence-backed records across three source systems.

    • Streamable HTTP endpoint with OAuth protected-resource metadata and a delegated, API-scoped token per user
    • Denied data is reported as withheld, never guessed or filled from another source
    • Conflicting sources are shown side by side with their evidence instead of being averaged
    • Server instructions kept under the 2,048 character limit Claude Code applies, enforced by a test

  • Connect-once business memory over MCP

    Monday.com · Yardi · SharePoint · Postgres

    Syncs a company's apps into typed facts in Postgres once, then hands them to any AI client over MCP.

    • Six tools: schema, brief, read-only SQL with a five second cap, document search, open conflicts, and resolve conflict
    • When sources disagree, a person answers once and the answer becomes a rule
    • Works in Claude Desktop, Claude Code, and any other MCP host

  • mcp-connector-check (open source)

    CLI · MCP server over stdio · CI

    A read-only checker Prestyj wrote that audits a remote MCP server for production readiness and security before it ships as a Claude connector or ChatGPT app.

    • Checks HTTPS, the unauthenticated 401 challenge, OAuth protected-resource metadata (RFC 9728), and authorization server metadata with PKCE S256
    • Flags tools without descriptions, untyped inputs, and write-looking tools not marked destructive
    • Only sends initialize and tools/list; it never calls a tool on the server it checks
    • Tested against a well-behaved and a badly behaved fixture server

Questions buyers ask

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Bring one workflow and the systems it touches.

Tell us what the work is, where its data lives, how much of it there is, and who signs it off. We come back with a written scope.

Book a scoping call