AI Automation & Agents
AI that does the work, not just the demo.
We automate business processes with AI: agents that use your tools, workflow integration into the systems you already run, internal RAG systems over your own knowledge, and MCP servers that make your platform AI-addressable.
Capabilities
From a process that costs you hours to a system that runs it
Most AI projects stall between the prototype and the part of the business that has to use it. We build the integration, the guardrails and the operations that close that gap.
- AI agents
- Tool calling
- MCP
- RAG
- Vector search
- OpenAI
- Anthropic
- Open-source models
- Fine-tuning
- Evals & guardrails
Business process AI automation
Take a process that eats hours, intake, triage, quoting, reconciliation, reporting, and automate the parts that follow rules, with a model handling the judgement in between. Measured against the process it replaces, not against a demo.
AI agents
Agents that use tools, not chatbots that guess. We define the tool surface, the retries, the permissions and the stop conditions, so an agent can query systems, take scoped actions and hand off to a human when it should.
Workflow integration
AI where the work already happens: your CRM, ERP, ticketing, telephony, storefront or internal admin. API-driven integration, event-driven triggers and human-in-the-loop approval steps instead of another tab to check.
Internal RAG systems
Retrieval-augmented generation over your own documents, tickets, code and databases: chunking and embeddings, vector search with metadata filters, permission-aware retrieval, and answers that cite their sources.
MCP servers & tool APIs
Model Context Protocol servers that expose your platform to AI assistants as typed, read-only or scoped-write tools, with authentication, rate limits and streaming responses. The clean way to make internal systems AI-addressable.
Document & back-office automation
Extraction, classification and validation across invoices, contracts, forms and support mail, structured output that downstream systems can trust, with confidence thresholds routing edge cases to people.
Evals, guardrails & observability
Evaluation sets that catch regressions before users do, prompt and model versioning, cost and latency budgets, plus tracing on every tool call so you can see what the system actually did and why.
Applied ML: prediction, language & vision
Where a model beats a rule: forecasting and risk scoring, transcription, sentiment and summarization, and detection and inspection pipelines for images and video, in production with full MLOps.
Proof · Picklebot
An MCP server that turns a platform API into a concierge
For the Pickleball.com ecosystem we built Picklebot: an MCP server that lets anyone ask in plain English and get an answer from live platform data.
Six read-only tools, tournaments, players, clubs, team leagues, ladder leagues and a support FAQ, are registered on an MCP server in front of the Pickleball API. A question like “4.0 mixed doubles tournaments in Florida open for registration” is routed to the right tool, the parameters are extracted from the sentence, live data is queried, and the answer streams back in natural language. A structured mode returns the same result as raw JSON so product surfaces can render cards instead of prose.
It is the pattern we reuse for internal systems: a typed tool surface, scoped authentication with cached tokens, read-only by default, and streaming responses, so the same server answers a person in chat and an application over an API. Picklebot is in development and being integrated into the Pickleball.com experience, more in the case study.
How we run it
Pilot in weeks, production with MLOps behind it
We start on one process with a measurable baseline, then harden what works.
A first engagement usually looks like this: pick one workflow, agree what “better” means in numbers, build the integration and the evaluation set alongside the model work, and put a human in the loop wherever a wrong answer would be expensive. Nothing goes live without tracing on every tool call and a rollback path.
Once it works, it becomes infrastructure: MLOps for the model lifecycle, GPU-enabled clusters where training or self-hosted inference is warranted, containerized pipelines on Docker and Kubernetes, and monitoring, versioning and governance, the same discipline behind the audit-grade verification we built into Roboticks.