AI Agents & Automation
Agents that act on your systems, with the guardrails enterprises require.
Agents that read, decide, and write to your business systems — with evaluation suites, human-in-the-loop checkpoints, and reliability targets you can hold us to.
What we ship
Concrete deliverables, not features.
01
Multi-step workflow agent
Agent that handles a workflow spanning CRM, ERP, ticketing, email, and internal databases. Carefully scoped, fully observable, with rollback paths and dead-letter queues.
02
Tool-using assistant for internal teams
Copilot for sales, support, operations, or engineering — backed by retrieval and your tool integrations. Built to amplify your team, not replace it. Adoption-tracked.
03
Eval infrastructure for agents
Every agent ships with an eval suite measuring task success rate, latency, cost, and safety against curated test cases. Eval-driven, not vibe-driven, and runs in CI.
04
Reliability engineering for production
Cost controls, rate limiting, retry/timeout logic, dead-letter queues, runtime monitoring, fallback paths. The engineering work that separates demos from systems that run for years.
How it works
Discovery first. Weekly cycles. Pause anytime.
- Discover01
Discovery sprint
Two weeks, fixed scope. Working prototype on your data, architecture decision record, scoped follow-on proposal.
- Design02
Build & integrate
Weekly cycles with demos and decision points. Scope can be adjusted, paused, or ended after any cycle. No surprises at the end.
- Build03
Ship to production
Code in your repositories. Deployment in your cloud by default. Runbook written for your team to operate, monitor, and extend it.
- Operate04
Optional retainer
Monthly retainer to maintain, monitor, and extend the system. Quarterly review against business outcomes. No auto-renew.
Where it compounds
Scenarios where this work pays back.
01 · Use case
Repetitive cross-system workflows
Your team spends hours moving data between CRM, ticketing, and internal tools — qualifying leads, updating records, opening tickets. Agents that do this reliably while a human supervises edge cases.
Typical: 60–80% of cases handled end-to-end, the remaining 20–40% escalated with full context.
02 · Use case
Internal copilots that compound across the org
Sales asks the agent for account research. Support asks for past similar tickets. Engineering asks for runbooks. One agent backbone, multiple tools, measured adoption.
Typical first-year scope: 3–5 tool integrations, measured weekly active users per team.
03 · Use case
Customer-facing agents with hard reliability targets
Agents inside your product, handling support, scheduling, or transactions. Eval suite enforces accuracy thresholds and refuses to ship updates that regress.
Typical: eval gates set at >95% task success, <2s p95 latency, <$0.04 per interaction.
Engagement
Fixed scope after discovery.
- Duration
- 3–6 months
- What you get
- Production agent platform, eval suite, integration with your systems, ops handover
- Start with
- Discovery sprint, 2 weeks
Frequently asked
Questions before the first call.
How do you keep agents from going off the rails in production?
Scope boundaries (tools and data the agent can touch are explicit), eval gates that block deploys regressing past a threshold, runtime observability with alerts on cost/latency/error rate, and human-in-the-loop checkpoints for high-stakes actions. Not all of these are needed for every agent — we choose based on blast radius.
Are agents actually production-ready in 2026?
For specific, scoped workflows with proper engineering — yes. For 'do anything for anyone' — no. We're explicit during discovery about which category your use case sits in. Most successful agents in production are narrow and well-instrumented.
Can the agent escalate to a human when it's uncertain?
Yes — that's standard. Confidence thresholds (or task-specific signals) trigger escalation with full context. The human sees what the agent tried, what it found, and what it recommends. Cleaner than a blank ticket queue.
What about cost? AI agents are expensive.
Cost depends on architecture. Caching, routing between cheap and expensive models, batch where latency allows, and prompt-engineering token-efficient flows usually bring per-interaction cost down by 3–10× vs naive implementations. We model cost during discovery and design to a budget.