Custom software development
Custom software development for the apps, APIs, and integrations around your AI workflows.
An 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.
Who this is for
- Companies whose AI workflow needs a review screen, an approval queue, or a dashboard that no product provides
- Teams with systems that do not talk to each other and need a maintained integration rather than another export
- Businesses that want an API or MCP layer over their own data so AI tools can use it safely
What we build
Examples of what can be built. These describe the kind of system, not case studies or client results.
Review and approval apps
A focused internal app where reviewers see the agent's output, the sources behind it, and the exception reason, then approve, edit, or reject with one action.
APIs over your data
A versioned API that exposes the records your workflows need with scopes, limits, and logging, instead of shared database credentials.
System integrations
Maintained syncs and webhooks between CRM, ERP, document stores, and databases, with conflict handling that surfaces disagreements instead of overwriting them.
Per-customer deployments
One codebase deployed separately per customer or business unit, with isolated databases and configuration-driven behavior.
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.
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
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.
- Enterprise AI automationAutomating 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.
- 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 ChatGPT pluginsOpenAI now lets companies add their own plugins to ChatGPT, built on MCP. We build the plugin for your internal systems, set it up for your workspace admins, and make sure each person only reaches what they already can.
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.