AI Agents
We build AI agents that read a request, look things up in your systems, take the action and hand off to a person when judgement is required — with guardrails you define and a full record of everything they did.
Beyond chat
A chat interface that explains your refund policy is useful. An agent that verifies the order, checks eligibility against the policy, issues the refund, updates the CRM and emails the customer is a different category of thing — it removes the work rather than describing it.
That difference is engineering, not prompting. It needs reliable tool access to your systems, retrieval over your real policies and data, permission boundaries, deterministic checks around non-deterministic reasoning, and an escalation path for everything the agent should not decide alone.
Typed tools with scoped permissions for your CRM, billing, ticketing and internal APIs.
Retrieval over your policies, product data and documentation, with citations back to the source.
Value limits, approval gates, blocked actions and confidence thresholds set by you, enforced in code.
Every step, tool call and decision recorded and replayable — for debugging and for audit.
What we build
Four agent patterns that cover most of what businesses actually need automated.
Agents that resolve tickets rather than deflect them: verifying accounts, checking order state, issuing refunds or replacements within policy, and escalating anything unusual with the context already gathered.
Agents embedded where your team already works — Slack, Teams, your admin tools — answering from internal knowledge, preparing documents, and completing the admin steps around a task.
Agents that qualify inbound leads, enrich records, keep the CRM current, draft follow-ups in your voice and flag the deals that have gone quiet before the forecast does.
Agents that gather, verify and summarise information at a scale people cannot sustain — monitoring sources, extracting structured data and producing briefs with citations you can check.
Operating standards
Autonomy is only acceptable with accountability attached.
Every tool call and decision recorded and replayable.
Any agent can be paused or reversed instantly.
No shared admin keys, ever — access is explicit per tool.
No change ships without scoring against your test set.
Agent engagement models
Start with one scoped task and a measured pilot. Widen the agent’s mandate when the numbers — not the demo — justify it.
One well-defined task, scoped and evaluated against your real historical cases before it touches live work.
from $890 /month
A measured pilot, not a proof-of-concept that dies in a demo.
A fully deployed agent working live inside your operation, with the observability and guardrails that make it safe to trust.
from $1,690 /month
The lane most teams move into once the pilot numbers hold.
A multi-agent estate with shared tooling, shared evaluation infrastructure and a roadmap owned alongside your team.
from $2,900 /month
Best for teams putting agents behind several parts of the business.
A pilot is deliberately narrow and time-boxed. If the evaluation shows a deterministic workflow would do the job more cheaply and reliably, we will tell you — and build that instead.
Why KeyPels
Most agent projects stall between an impressive demo and something the business can rely on. This is what closes that gap.
We build a test set from your real cases and score the agent against it on every change. Without evaluation you are not improving an agent, you are rearranging prompts and hoping. Regressions get caught before your customers find them.
Agents act with scoped credentials, per-tool boundaries and value limits — not a shared admin key. What an agent may do is defined explicitly, enforced in code, and reviewed like any other access decision.
Money movement, data deletion and legal commitments run through conventional code with hard rules. Language models decide what should happen; validated logic decides whether it is permitted.
When an agent hands off to a person, it hands off the full picture — what was asked, what it checked, what it concluded and why it stopped. No customer repeats themselves to a human afterwards.
Model routing, caching and context management are treated as design constraints. An agent that is brilliant but slow and expensive does not survive contact with production volume.
How we work
Narrow scope, measured accuracy, then widened authority as it earns trust.
We pick one well-defined task with a measurable outcome, and agree explicitly what the agent may and may not decide.
Typed, permission-scoped integrations with your systems, plus retrieval over the knowledge the agent needs to be right.
A test set built from your real historical cases, scored on every iteration, with a target agreed before launch.
The agent runs with a human reviewing its actions, so accuracy is proven on live work before authority widens.
Approval gates relax as the numbers justify it, and the next task joins the agent’s scope.
FAQ
Still unsure? Send us a note — we reply personally.
A chatbot converses. An agent acts — it can call your systems, look up records, complete a multi-step task and produce a result. A refund chatbot explains the policy; a refund agent verifies the order, checks eligibility, issues the refund and updates the CRM. Both have their place, but they are different engineering problems.
Permission scoping per tool, hard value and rate limits, explicit blocked actions, approval gates on anything irreversible, and deterministic validation around every consequential step. During pilot, a human reviews actions before they execute; that gate only relaxes once measured accuracy justifies it.
It escalates rather than guesses when confidence is low or a case falls outside its scope, handing a person the full context. Actions are logged and reversible, and every failure feeds back into the evaluation set so the same mistake is caught automatically on the next release.
We stay model-agnostic and choose per task — frontier models where reasoning quality matters, smaller or open-weight models where cost, latency or data residency dominates. The architecture keeps model choice swappable, so a pricing or capability change does not mean a rebuild.
No. We use enterprise API tiers with training disabled, and where policy or regulation requires it we run open-weight models entirely inside your own infrastructure. Data flow and retention are documented before development begins.
A scoped pilot on one task typically reaches a working state in about three weeks, then runs with human oversight while we measure accuracy against your real cases. Widening its mandate is a decision driven by those numbers rather than a fixed timetable.
Yes. Agents connect through APIs, webhooks or a middleware layer we build for systems that lack one — CRMs, ticketing, billing, ERPs and internal tools. Replacing your existing stack is not a prerequisite.
Name the task that eats your team’s day. We will scope an agent for it, define the guardrails and show you the numbers from a pilot.
