Assistants that know your business and know when to stop.

We build customer-facing support and sales chatbots, internal-ops assistants that sit on top of your tools, and task-specific agents that take actions rather than just answer questions. Each is scoped to a real workflow, not a generic FAQ bot.

A chatbot answers questions in a conversation. An agent can also take action — look up an order, update a record, trigger a workflow — using tools you give it access to. Most builds we ship are agents with a chat interface on top.

We're model-agnostic and pick based on your use case, latency, and budget — typically from Anthropic, OpenAI, or open-weight models self-hosted for cost or data-residency reasons. We benchmark candidates against your real conversations, not a generic leaderboard.

Yes — we train on your historical ticket data and knowledge base to identify the highest-volume, lowest-risk tickets to automate first, then expand scope as accuracy is proven out.

We integrate via API, webhook, or native connector into your CRM, helpdesk, database, or internal tools — Zendesk, HubSpot, Slack, Postgres, and custom internal systems are all common. The agent works from your live data, not a static snapshot.

We scope exactly what data the agent can read and write, use provider-level data-retention opt-outs where available, and can deploy self-hosted or region-locked models when compliance requires it. Access is scoped per integration, never blanket.

We ground responses in your actual documents and data via retrieval rather than relying on model memory, constrain the agent's action space to approved tools, and run an eval suite against known-good answers before and after every prompt change.

Every build includes a defined escalation path — handoff to a human, a ticket created automatically, or an honest "I don't know" — so the agent never guesses on something it can't verify.

We set an accuracy target during scoping and don't go live until the eval suite hits it. Most launches start with a narrower scope of high-confidence use cases, then widen as real conversation data comes in.

A focused single-use-case agent typically ships in 3–6 weeks. Multi-tool internal-ops agents or agents integrating several systems usually run 6–10 weeks. We scope the exact timeline before kickoff.

Cost depends on scope — number of integrations, conversation complexity, and whether ongoing monitoring is included. We give a fixed project quote after the discovery call, plus model API usage which scales with volume.

Yes — conversation review, prompt tuning, and model updates are typically handled through a monthly retainer so accuracy holds up as your product and edge cases evolve. Ad hoc support is available if you'd rather run it in-house after handoff.

Yes. We regularly take over agents built in-house or by another vendor — auditing prompts, integrations, and failure logs first, then fixing accuracy and reliability issues without a full rebuild where possible.

We're a startup-focused team that treats agent accuracy as an ongoing product, not a one-off deliverable — evals, guardrails, and monitoring ship with every build, and our product and engineering leads stay hands-on through launch and beyond.

Book a scoping call — we'll walk through the workflow you want to automate, the systems it touches, and give you a fixed-scope proposal and timeline within a few business days.