Assistants on your data
Support, sales, onboarding, and internal assistants grounded in your docs, catalog, policies, records, and knowledge base.
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AI agents & workflow automation.
We build secure AI agents and automations that plug into your real data, tools, and workflows — not demos.
What we build
Three ways AI becomes useful inside the business.
Agent workflows
AI that does work, not just talk: ticket triage, refund pre-checks, listing enrichment, lead research, reporting, and admin drafts with approvals.
Custom MCP servers
MCP servers that let AI tools operate your SaaS or API through authenticated, rate-limited, permissioned tools instead of fragile browser hacks.
The difference
The hard part of AI isn't the model. It's the system around it.
Anyone can call an AI API. The engineering is in what surrounds it: keeping keys off the client, capping costs, rate-limiting abuse, grounding answers in your real data, refusing off-topic use, logging for audit, and making sure an agent can never take an action you wouldn't approve. That systems layer is what we build — the same standard we apply to payment code.
0API keys in the browser
Hard capson tokens & spend
Audit logevery agent action
Human gateon money-moving steps
Process
From idea to a working AI feature.
Scope the job
Which task, which data, what the AI may and may not do. We write the guardrail spec first.
Output: AI feature specGround it
Your content and data become the assistant's knowledge; retrieval tested against real questions.
Output: grounded prototypeIntegrate
Wired into your product: UI, backend proxy, rate limits, cost caps, monitoring, fallbacks.
Output: production featureMeasure & tune
Real usage review: answer quality, deflection rate, cost per conversation, prompt iteration.
Output: tuning reportEngagement
Scoped, fixed, no surprises.
Every AI engagement starts with a written spec — what the AI may do, what it may never do, and what it costs to run monthly with hard caps we set together. The fixed-price AI-readiness audit is the lowest-risk entry; assistants, agent workflows, and MCP servers are quoted per scope after one conversation about your product.
Questions
Asked before hiring us.
What is an MCP server and why would my product need one?
MCP (Model Context Protocol) is the standard that lets AI tools like Claude and ChatGPT operate real software — adopted across the industry. An MCP server makes your SaaS or API usable by every AI assistant, with authentication and safe tool design. Early movers get found by the agent ecosystem first.
Which models do you build on?
Anthropic Claude and OpenAI models, chosen per use-case and cost. Your keys, your infrastructure, hard spending caps.
Is my business data safe?
Your data stays on your infrastructure; the AI provider only sees what a single request needs. No third-party chatbot platform sits in the middle, and nothing is used to train models.
Can an agent safely touch orders and payments?
Only with guardrails: scoped permissions, idempotent operations, human confirmation on money-moving actions, and full audit logs. That safety layer is exactly what we engineer — it's the payments discipline applied to AI.
What does it cost to run monthly?
Typically $5–50/month in API usage at small-business volume. We set hard caps so a traffic spike can never surprise you.
Have a job AI should be doing in your product?
Describe it in two lines. We'll reply with a concrete approach within 1 business day.