Built AI · Service
AI Agents. Software that gets work done.
Answer
We build production AI agents that execute real workflows — research, sales, operations, finance, support and executive intelligence — connected to your data, your tools and your guardrails. Not chatbots. Operators.
Agent matrix by business function
Different functions demand different defaults for autonomy, tool access and human oversight. This is the matrix we work from when scoping agent systems — the starting position, not the ceiling.
| Function | Example agents | Default autonomy | Tool access | Risk profile |
|---|---|---|---|---|
| Research | Market scans, deal sourcing, account research, competitive intelligence | Medium — generates outputs autonomously, humans curate | Web search, internal data, CRM read | Low |
| Sales | Outbound personalisation, follow-ups, meeting prep briefs, CRM hygiene | Medium — drafts in queue, humans approve sends | CRM read/write, mailbox draft-only, calendar | Medium |
| Operations | Ticket triage, data reconciliation, vendor coordination, internal request handling | High on read, gated on write | Ticketing, ERP read, internal APIs | Medium |
| Finance | Invoice classification, expense review, anomaly flagging, reporting drafts | Low — assistive only | Read-only ledger, draft journals, no payment authority | High |
| Customer support | Tier-1 deflection, ticket routing, draft responses, knowledge updates | Medium — autonomous on Tier-1 deflection, draft on escalations | Help desk, knowledge base, customer data scoped per ticket | Medium |
| Executive intelligence | KPI digests, board prep, signal monitoring across portfolio or business units | High on synthesis, no external action | Data warehouse read, BI APIs, document generation | Low |
How human-in-the-loop is designed
Human oversight is not a slider. It is a set of explicit rules wired into the agent's tool layer. Every agent we ship answers three questions before any action: what is the action, what is its blast radius if wrong, and who must approve it. Reversible read-only actions run autonomously. Irreversible writes — sending a message, moving money, changing a contract — require a human decision in the loop.
Approval gates
Specific tool calls require named human approval before execution. Recorded in the audit log.
Confidence thresholds
Below a defined confidence score, the agent escalates to a human instead of acting.
Time-boxed autonomy
Agents operate inside scoped windows and budgets. Beyond those, they pause and ask.
Tool access and data security
Agents are only as safe as the tools they can call. Every tool an agent uses is a scoped credential with the minimum permissions required for the task — never a shared admin token. Read access is granted by default, write access is granted by exception, and destructive operations are gated behind approval flows.
Customer and operational data stays inside your environment. Sensitive workloads run inside your VPC or on-prem. We do not use client data to train external models, and prompt and tool-call traces are stored under your retention policy, not ours.
What "production-ready" actually means
Most agent demos are not production systems. The difference is measurable. Before we hand over an agent, it has all of the following — and we walk your team through each one.
- Defined success metrics and a baseline against them
- An evaluation suite that runs on every prompt, model or tool change
- Full observability for every tool call, with cost and latency
- Rate limits, budget caps and circuit breakers
- Rollback path to the previous known-good configuration
- Written escalation and incident-response policy
- Audit log of every action taken on behalf of a human
FAQ
How is an agent different from automation?+
Automations follow fixed rules. Agents reason, plan and adapt within guardrails. We use both — automation where deterministic flows are safer and cheaper, agents where judgement and context matter.
How do you keep agents safe?+
Tool-level permissions, scoped credentials, human-in-the-loop checkpoints on irreversible actions, full audit logs and evaluation suites running on every change to prompts, models or tools.
Which models do you use?+
We are model-agnostic. We pick the best frontier or open model per task and re-evaluate as the landscape changes. Architecture is designed so the underlying model can be swapped without re-engineering the system.
Where does the data live?+
Inside your infrastructure. We do not require data to leave your environment, and sensitive workloads run inside your VPC or on-prem if needed.
What is production-ready for an agent?+
Defined success metrics, an evaluation suite that runs on every change, observability for every tool call, rollback paths, rate limits and a written escalation policy. Without those, it is a prototype.