We build agents that plan a task, use your real systems as tools and complete multi-step work end to end — inside guardrails you set, with every action logged, approvable and reversible.
Where agents differ
A chatbot ends its turn with a sentence. An agent ends it with a refund issued, a record updated, a supplier chased or a report filed. That difference is the entire value — and the entire risk, because a system with permission to change your data needs more than a good prompt behind it.
So we build the boundary first. Each agent gets a narrow, explicit toolset, hard limits on what it can touch, an approval gate on anything consequential, and a complete trace of what it did and why. Then we prove it against real historical cases before it acts on a live one.
Agents get a small explicit set of functions against your systems — never open-ended access to everything.
Value limits, allow-lists and approval gates are defined and tested before the agent is trusted with anything.
Plan, tool calls, inputs, outputs and decisions recorded, so any outcome can be replayed and explained months later.
Actions are undoable or compensable, and any run can be paused, corrected or rolled back by a person.
What we build
Four agent types covering the work that needs more than an answer.
Agents that resolve rather than reply: look up the order, check the policy, issue the refund, update the record and close the ticket — with a value threshold above which a person approves first.
The chasing, checking and reconciling that fills an operations week — invoices matched to purchase orders, exceptions investigated, suppliers chased, discrepancies escalated with the evidence attached.
Agents that gather from your own data and approved external sources, check claims against them, and produce a cited brief — for account preparation, competitor tracking or tender qualification.
An agent embedded where your team already works, able to draft, look up and take the routine actions — with the person in the loop on every step it proposes, until they choose to let it run.
Operating standards
Autonomy is only acceptable with a boundary around it. These are the guarantees behind every agent we ship.
Plan, tool calls, inputs and outputs recorded on every run.
Any run can be paused, corrected or reversed by a person.
Scored against real historical cases before it touches a live one.
Monitored with alerting on failures, loops and silent stalls.
Engagement models
Prove one agent on one task, then widen the boundary once the evidence supports it.
One narrowly scoped agent, fully instrumented, running supervised on real work.
from $790 /month
The smallest deployment that proves the boundary holds.
An agent trusted to act within limits, monitored, with the gates relaxed only where evidence supports it.
from $1,290 /month
The lane most teams reach after a successful pilot.
A standing team building and operating a fleet of agents across your business.
from $2,190 /month
Best for operations running several agents at once.
Plenty should not be. If a process is deterministic, a plain automation is cheaper, faster and easier to reason about — and we will say so rather than putting a model where an "if" statement belongs.
How we work
Capability is the easy half. We spend the effort on the boundary, the evaluation and the audit trail.
One job, defined precisely: what counts as done, what must never happen, and which cases always belong to a person.
The narrow function set the agent may call, the value and volume limits around it, and the approval gates on anything consequential.
The agent is built with structured tracing from the first commit, so every plan and tool call is inspectable while we develop it.
Scored against real historical cases with known correct outcomes, including the adversarial ones you would not want it to get wrong.
It proposes, a person approves, and we measure the agreement rate until the boundary is proven with your data.
Gates relax only where the evidence supports it, with alerting, spend limits and a kill switch left permanently in place.
Outcomes
The difference between a system that drafts the work and one that finishes it.
Cases closed end to end instead of being classified and handed to the next queue.
Chasing, checking and reconciling done before anyone opens a laptop in the morning.
The same policy applied to the thousandth case as to the first, with the reasoning recorded either way.
Your team spends the day on exceptions and relationships rather than on the predictable middle.
Every decision replayable with the evidence it used — for a customer, an auditor or a post-mortem.
Spend limits, allow-lists and approval gates mean the worst case is a small, reversible mistake.
FAQ
Still unsure? Send us a note — we reply personally.
A chatbot produces an answer; an agent produces an outcome. It plans a task, calls real functions against your systems, checks the result and continues until the job is done or it hits a boundary. That means it needs permissions a chatbot does not — which is why we design the guardrails before the capability.
Layered limits. Each agent gets a narrow, explicit toolset rather than open access; destructive operations sit behind approval gates; value and volume caps apply per run and per day; allow-lists restrict what it can reach; and everything is logged. Actions are built to be reversible or compensable, and there is always a kill switch.
We score it against real historical cases where the correct outcome is already known, including deliberately awkward ones. It then runs supervised — proposing actions a person approves — until the agreement rate justifies relaxing a gate. Autonomy is earned with evidence, not assumed.
Multi-step work that needs looking things up across systems and applying judgement within known rules: support resolution, exception investigation, reconciliation, research and qualification. Deterministic processes are better served by plain automation, and we will tell you when that is the case.
Yes. Agents act through the same APIs your applications use, plus middleware where a system has none. They sit alongside your stack rather than replacing it, and they respect the same permissions your staff have.
Model usage is metered per run, so we design for it: smaller models where they are sufficient, caching on repeated context, and hard spend caps per agent. Cost per completed task is reported alongside accuracy, so you can see both sides of any tuning decision.
No. We use enterprise API tiers with training disabled, keep data inside your infrastructure wherever practical, and can run open-weight models in your own environment when policy or regulation requires it. Data handling is documented before the build starts.
Name the process that eats your team’s week. We will scope it, define the boundary and show you what an agent can safely own.
