Custom AI agents
Custom AI agents, built and run for the work your team repeats every week.
We build agents around one defined workflow at a time: they read from your existing systems, do the work, hold anything uncertain for a person, and hand back a finished output with its sources. Then we keep them running.
Who this is for
- Teams with a recurring workflow that pulls from two or more systems and ends in a document, update, or decision
- Operators who have tried an off-the-shelf AI tool and found it could not reach their data or follow their rules
- Companies that want the work done and maintained, not a platform to configure themselves
What we build
Examples of what can be built. These describe the kind of system, not case studies or client results.
Inbound lead research and routing
Reads a new form fill, researches the company from public sources and your CRM history, drafts a qualification summary, and routes it to the right owner for review.
Weekly reporting pack
Pulls figures from the CRM, ad platforms, and finance system, reconciles the totals, drafts the commentary, and holds the pack until the report owner approves it.
Document intake and extraction
Classifies incoming invoices, contracts, or forms, extracts the fields you define, checks them against the system of record, and queues mismatches.
Question answering over company systems
A governed agent that answers staff questions from your CRM, ERP, and document store with the source record attached, and says when data is withheld.
How an engagement works
Every engagement is scoped per workflow, source systems, volume, and control requirements, and priced in writing after scoping.
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.
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.
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.
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.
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
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
Agents by function
- AI sales agentsReps 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.
- AI marketing agentsMarketing work is full of repeatable assembly: pulling campaign numbers, writing briefs, resizing and versioning assets, keeping the content calendar honest. We build agents for that assembly and keep publishing decisions with your team.
- AI operations agentsOperations 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.
Connectors
- Custom MCP serversOne well-built MCP server gives Claude, ChatGPT, Cursor, and your own agents the same governed access to your data and actions. We design it around your workflows, secure it like any other production API, and run it.
- Custom Claude connectorsThe Claude directory covers the popular SaaS apps. Your ERP, your database, your internal API, and the spreadsheet that runs operations usually have no connector at all. We build one, host it, and roll it out to your Claude Team or Enterprise workspace.
Institutional real estate
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.