Full-service agents for real operations
We scope, build, and manage custom agents for high-value processes with messy data, edge cases, integrations, and human review.
Built by people who have shipped in high-stakes environments.
The problem
The best agents are built from workflows that already matter: recurring reporting, reconciliations, review packets, intake queues, and decisions that need a clear audit trail.
We build around the messy parts: exceptions, approvals, source data, system access, cost control, and the operating rhythm that keeps the agent useful after launch.
Examples
Match records across finance and operations tools, explain discrepancies, and flag exceptions before human sign-off.
Pull trusted source links, surface anomalies, and draft a plain-English brief the team can review before it circulates.
Extract terms, compare documents, prepare review-ready summaries, and leave final calls with the people responsible.
How it works
Paid scoping / fixed fee
Learn the workflow, define success, map systems and data, and estimate the value before you commit to a build.
Fixed-fee build
Design, integrate, test, and launch the agent with review loops, source checks, and practical guardrails.
Monthly management
Monitor behavior, control costs, tune workflows, and improve the agent as the business changes.
No pitch deck. Just a conversation about the workflow worth fixing.
Why sammartin.ai
We combine hands-on AI engineering with experience evaluating technical systems and real business workflows. That judgment shapes agents built for practical use, active monitoring, and operating value.
We know when a lightweight agent is enough, and when a workflow needs deeper engineering.
Human approval stays in the process where judgment, risk, or context matters most.
We measure what matters and catch issues before they quietly affect the business.
Architecture and token discipline keep ongoing costs predictable enough to make sense.
Partners we've helped
Case study
In roughly six weeks, we built a matter-scoped collaboration platform that keeps AI assistance inside attorney-controlled workflows, with context selection, attorney review, audit records, AWS deployment runbooks, and Amazon Bedrock integration.
Read the case study
AI Scientist & Engineer
Sam is an AI researcher, operator, and investor with nearly a decade of hands-on machine learning in high-stakes settings. He co-invented Random Contrast Learning at Lumina AI and has applied ML to quantitative trading, cancer detection, and threat-detection systems used in federal and state environments, and has presented on practical AI for defense and national-security audiences.
He has led complex development organizations, built secure client-facing communication platforms, and runs Eidetic Ventures, investing across AI, blockchain, and space.
"Building a demo is easy. Everything that comes after takes discipline and expert knowledge to do well. When I work with your team, we start by finding where AI is actually worth implementing - and where it isn't - and estimate the return on investment before you commit to anything.
From there we build toward your goals rather than a generic spec, and once it's live we put a maintenance plan in place to support your team for the long term."
Contact
Send us a note. We will learn about your process, share what looks possible, and advise on next steps.
We reply within 1 business day.
