The Platform
Map the company.
Build the agents.
Run them in production.
Plainsight is the audit layer. humans.ai is the build-and-run layer. Together they take an enterprise from “we should be using AI” to AI agents quietly running parts of the operation — without leaving the customer to figure out which agents to build, what to host them on, or how to monitor them.
Each phase has a real artifact. The map is a URL. The blueprint is a URL. The agents are running on the customer's infrastructure. Nothing lives in a slide deck.
Owner
Plainsight
Duration
1 week
Map the company.
A trained voice agent runs a 20-minute conversation with every person on the team. The map builds itself in real time as interviews complete. By the end of the week, the leader has an operational twin of how their company actually runs — every workflow, every handoff, every piece of knowledge that lives in one person’s head.
- 20-minute voice interviews on each person's schedule. Confidential by design.
- Live map at /status/[company-slug] — leader watches nodes light up.
- Per-interview structured extraction (role, top workflows, tools, one quote).
- Company-level analysis: 3 top findings, 5–13 mid findings, 3 build-ready agents.
Artifact
The blueprint
A live URL with the full operational map, the patterns, and the three highest-value AI agents to build first — in order, with hours-recovered estimates and trust-budget reasoning.
Owner
humans.ai
Duration
2–14 weeks
Build the agents.
humans.ai takes the blueprint and ships the three agents in build order. Lowest-political-risk first, so the firm sees value before being asked to trust an agent with anything sensitive. Each agent is deployed inside the customer’s existing tools — Gmail, Salesforce, SharePoint, Slack, the things their team already lives in.
- Off-the-shelf MCP servers where they exist (Notion, Linear, GitHub, Gmail, Slack, Salesforce, QuickBooks, Stripe).
- Custom MCP wrappers when needed — usually 1–2 days of work per integration.
- Sandboxed compute via Anthropic Managed Agents for multi-step workflows that need file I/O.
- Model selection per agent: Haiku 4.5 for high-volume extraction, Sonnet 4.6 for production defaults, Opus 4.7 where reasoning quality justifies it.
- Human-in-the-loop on every outbound action for the first 90 days. Kill switches built in from day one.
Artifact
Agents running in production
Each agent ships into the customer's existing systems with a defined SLA, an observability dashboard, and a documented rollback path. No new dashboard the team has to learn — the agent shows up inside Gmail, inside Slack, inside the tools they already use.
Owner
humans.ai
Duration
Continuous
Run them as living systems.
An agent that shipped six months ago isn’t the same agent today. Models improve. Workflows shift. Edge cases surface. humans.ai operates the agents as living systems — monitoring outputs, auditing behaviour, and retraining when the operating environment changes.
- Continuous LLM-as-judge audits of every agent decision sampled at meaningful rates.
- Quarterly Plainsight refresh — re-map the team to catch drift, surface new patterns, scope the next agent.
- Model upgrades when the underlying foundation model ships a better version (cheaper / faster / sharper).
- Compliance, SOC 2 evidence, kill switches, role-based access — built in, not bolted on.
- Single accountable team: the same humans.ai people who built the agents run them.
Outcome
Agents that compound
Year one: 3 agents recover 15–25% of the team's translation work. Year two: 8–12 agents, with each new agent informed by the data the earlier ones produced. The map gets sharper. The blueprint gets more specific. The agents get cheaper to run.
Where humans.ai sits in the stack
We’re the integrator that takes an enterprise from intent to outcome — without the customer having to choose vendors, host inference, or wire the plumbing.
Most enterprises trying to adopt AI agents fail somewhere in the gap between “we picked a use case” and “an agent is doing it in production.” That gap is not a model problem — it’s a discovery problem, an integration problem, and a trust problem stacked together. humans.ai is the team that absorbs all three.
Discovery
Plainsight
Map the company. Identify the patterns the agents should target. Rank them.
Build & Integration
humans.ai
Compose models, MCP servers, sandbox compute, and the customer's existing tools into agents that ship.
Infrastructure
Foundation partners
Anthropic for the model layer. NVIDIA NIM / hosted inference for compute economics at enterprise scale. Customer's existing identity, data, and observability stack.
One contract. One team. The customer gets a map, agents running on the right infrastructure, and a managed operation that compounds value over time.
Acompany
Start with a map.
The rest follows.
Your first team is free. About twenty minutes per person. One week from start to blueprint.