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PRESTYJ

AI sales agents

AI sales agents that do the research, drafting, and CRM upkeep your reps skip.

Reps lose hours to looking up accounts, writing the same follow-ups, and updating fields nobody checks. We build agents that do that groundwork from your CRM and approved sources, and leave every customer-facing message with the rep.

Book a scoping call How an engagement works

Who this is for

  • Sales teams whose CRM data is incomplete because updating it competes with selling
  • Revenue operations leads who rebuild the same pipeline report by hand each week
  • Founders and sales managers who want faster lead response without unreviewed outreach

What we build

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

  • Lead research and qualification

    For each new lead, gathers firmographics from approved sources and prior activity from the CRM, scores it against your written criteria, and attaches the reasoning so a rep can disagree quickly.

  • Follow-up drafting

    Drafts the follow-up after a call or meeting from the notes and the account history, in your templates, and places it in the rep's queue. Nothing is sent without the rep.

  • CRM hygiene

    Finds duplicate accounts, stale stages, missing next steps, and contacts that have changed roles, then proposes fixes for an owner to approve rather than overwriting records.

  • Pipeline reporting

    Builds the weekly pipeline view from the CRM, flags deals that moved backwards or have gone quiet, and drafts the commentary for the sales leader to edit.

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.

  • 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

  • Governed context server for a research platform

    Versioned context API · Webhooks

    A stdio MCP server with 24 workflow-specific tools that never sees a database URL or tenant ID.

    • The API key resolves the tenant; caller-supplied tenant or access fields are rejected before execution
    • Every tool enforces a scope plus result, time, output-token, and cost ceilings
    • Read-only by default; writes need a separate switch, explicit confirmation, and an idempotency key
    • A tool-selection benchmark checks that the model picks the right tool for each question

  • 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

Related

Custom AI builds

Related builds

Connectors

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