Enterprise AI automation
Enterprise AI automation across your systems, with the permissions and audit trail security will ask for.
Automating one task is easy. Automating work that crosses identity, CRM, ERP, and document systems is a controls problem. We design the permissions, approvals, logging, and rollback first, then the agent.
Who this is for
- IT, security, and operations leaders asked to approve AI automation that touches several systems
- Organizations with SSO, role-based access, and change-control rules any automation must respect
- Teams replacing brittle scripts or RPA bots with agents that explain what they did
What we build
Examples of what can be built. These describe the kind of system, not case studies or client results.
Cross-system workflow orchestration
An agent that reads a request, gathers data from several systems under the requesting user's permissions, prepares the change, and routes it to the right approver.
Per-user access through SSO and OAuth
Users sign in through your identity provider, and every tool call runs with a delegated, scoped token, so the agent can never see more than the person asking.
Audited writes with rollback
Every write is confirmed, idempotent, and logged with its before state, so a change can be reviewed and reversed rather than reconstructed.
Governed AI access for staff
A single governed endpoint that lets Claude or ChatGPT reach approved company data with rate limits, size limits, and per-tool scopes.
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
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
Multi-tenant MCP for isolated customer deployments
Per-customer deployment · Per-customer database
One codebase deployed separately for each customer, with an MCP endpoint whose tool catalog comes from that customer's configuration.
- OAuth token introspection with a dedicated read scope
- Per-token rate limits and bounded request sizes
- No customer data or secrets in the shared code or deployment packs
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
- Custom AI agentsWe 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.
- 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.
- Custom software developmentAn agent is only useful if people can review its work and systems can exchange data reliably. We build the surrounding software: review queues, internal apps, APIs, and integrations, as production code with tests.
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.
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.