Agents, retrieval, fine-tuning, and integration — engineered into real products and shipped with the evaluations and observability that tell you it actually works. No demos that fall over in production.
A flashy AI prototype takes a weekend. Making it reliable, measurable, cost-controlled, and safe enough to expose to customers takes engineering. We build AI systems the way we build software: with evaluation suites, tracing, guardrails, and a clear view of cost and latency — so "it works on my machine" becomes "it works for your users."
We're model-agnostic: Anthropic Claude, OpenAI, and open-source models — chosen per job, not per hype cycle.
Agents that do real work. Autonomous and semi-autonomous agents with MCP-based tool orchestration and multi-agent architectures — able to plan, call your tools and APIs, and complete multi-step tasks within guardrails you control.
Answers grounded in your data. Retrieval-augmented generation pipelines with vector databases (Pinecone, pgvector), embeddings, and domain-specific fine-tuning — so the model speaks your domain and cites your sources.
Chat and voice that can actually act. Context-aware assistants with function calling and CRM/helpdesk integrations, plus voice interfaces — support, sales, and internal assistants that resolve, not just reply.
Add intelligence to the product you already have. We embed LLM capabilities into existing products through clean API orchestration across OpenAI, Anthropic, and open-source models — without a rewrite.
Cut the manual work out of your operations. Event-driven automation with n8n, Make, or custom pipelines; webhook architectures; and internal tooling that connects your stack and removes repetitive steps.
Know it will work before you fund the build. Feasibility assessment, model selection, cost/latency optimization, and evaluation frameworks — a clear-eyed plan for where AI pays off and where it doesn't.
Define success and how we'll measure it before building.
With your data, your constraints, your edge cases.
Guardrails, tracing, cost/latency tuning, and an eval suite in CI.
Deployed with monitoring and human-in-the-loop where it matters.
Measured accuracy on your data, regression-tested per commit.
Every LLM call, tool call, and hop visible in your observability stack.
Input validation, output filters, and human-in-the-loop where it matters.
Per-request budgets enforced, monitored, and alerted on.
Interfaces users trust and developers can build without guesswork.
ExploreProduction software on Next.js/React/TypeScript with CI/CD from day one.
ExploreSEO/AEO, analytics infrastructure, and growth engineering.
ExploreTell us what you're working on. We'll show you the fastest credible path to shipping it — even if that's smaller than you expected.
hello@sparzan.com