Teams with a real product or internal tool to ship.
- New products that need design, build, and launch in one motion.
- Internal tools or CRMs replacing spreadsheet-and-Slack chaos.
- Founders who want one team owning the outcome, not just tickets.
Every business runs on a thousand small decisions — is this lead hot? does this ticket need escalation? what did that sales call really say? We build AI agents that make those calls instantly, 24/7, with confidence scores and human-approval gates where it matters.
These are the agents we build most often. Each one replaces a chunk of human judgment that doesn't need to be human.
Reads the form / chat / call transcript, scores intent + fit against your ICP, ranks 0–100 with reasoning. Hot leads flagged to AEs in Slack within 30 seconds.
Incoming tickets / emails / DMs classified by topic, urgency, and language, then routed to the right human (or auto-resolved if simple). Response time drops 80%.
Sales calls, support chats, internal meetings — summarized with action items, sentiment, and follow-up drafts. Everything stored in CRM with the transcript attached.
Approve / reject with reasoning: refund requests under $X, discount asks, content moderation, access grants. Low-stakes decisions auto-execute; high-stakes escalate with a recommended action.
Agents are only useful if you trust them. We don't turn on anything autonomous until we've watched it make the same decision a human would, on shadow traffic, for two weeks.
Week 1. We sit with your team and document the decision in detail — the inputs, the reasoning, the edge cases, the escalation rules. This is the agent's job description.
Week 1–2. We pull 100–500 historical examples from your CRM / inbox / call logs, labeled with the correct decision. This is what we evaluate the agent against.
Week 2. Prompt engineering, structured output schemas, tool calls, confidence thresholds. Evaluations run automatically against the training set on every change.
Week 3. Agent runs on real production traffic but doesn't act — we log its decisions and compare to the human decisions. Disagreements are where we tune.
Week 4. High-confidence decisions go auto, low-confidence escalates to a human, everything logged. Rollout to 20% → 50% → 100% as trust builds.
Ongoing. Every decision logged with reasoning, sampled for quality, retrained quarterly. Drift caught before it hurts.
Sierra Roof was drowning in form submissions — most noise, some gold, no time for the sales team to sort them. We built a lead-scoring agent that reads every intake, scores intent + urgency + fit against their ICP, and flags the top ~5% in Slack with a recommended opening line. The sales team went from 'chase everything' to 'work the hot 12 today'. Close rate doubled; hours-to-first-contact dropped from 18 to 0.4.
We charge to build and evaluate the agent well. LLM API calls are pass-through (usually $0.005–0.05 per decision). No markup.
We engineer for safety. Three guards: (1) confidence thresholds — below X, escalate to a human; (2) structured outputs with validation — the agent can't return malformed data; (3) shadow mode for 2 weeks before any autonomous action. For high-stakes decisions (refunds, legal, medical), the agent recommends; a human approves.
Every agent has an evaluation set — 100–500 labeled historical examples. On every change to the agent, we re-run the full eval set and compare. We don't ship changes that drop accuracy. You see the eval dashboard and can set the threshold we won't fall below.
Yes — through two channels. (1) We add disagreements from shadow mode to the eval set, retuning the prompt. (2) For high-volume agents, we fine-tune a smaller open-source model on your labeled data, which often beats a frontier LLM on your specific task at 1/10th the cost.
Depends on the task. GPT-5 or Claude Opus for nuanced reasoning; Claude Haiku or Gemini Flash for high-volume / cost-sensitive; fine-tuned open-source (Llama / Mistral) for domain-specific tasks where data privacy matters. We'll recommend per use case.
Yes. For regulated industries (health, finance, legal) we can deploy entirely on your VPC, using self-hosted open-source models via Ollama or vLLM. No data leaves your network. We've done it for clients with HIPAA, SOC 2, and ISO 27001 requirements.
Agents do specific jobs with specific inputs, outputs, and accountability — not open-ended chat. Our scoring agent takes a lead object, returns a score + reasoning + confidence, logs the decision, and integrates with your CRM. It's a production service with SLAs, not a chatbot.
30 minutes. We walk through the decisions your team makes every day, identify the one costing you the most time, and sketch what an agent would look like for it.