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AI Automation
AI Agents & Copilots
Agents that qualify leads, answer questions and move deals 24/7.
Overview
What we do & why it matters
AI agents extend your team without adding headcount — qualifying leads, answering questions and progressing deals around the clock. We build and integrate them with guardrails and human-in-the-loop where it counts.
What’s included
- Use-case discovery & design
- Agent build & integration
- Knowledge-base / RAG grounding
- Guardrails & human-in-the-loop
- CRM & tool connections
- Monitoring & iteration
Part of our AI Automation engagement · from transparent pricing.
What an agent is good for
Bounded tasks with checkable output.
An agent earns its place when the task is repetitive, the inputs are available, and the output can be verified — research summaries, ticket triage, data extraction, first-draft documents. Open-ended autonomy over business-critical decisions is not a use case, it is a risk.
Scope written as rules
What the agent may read, may write, must escalate and must never claim — defined before it goes near production data.
Verifiable output
If nobody can tell whether the result is right, the agent cannot be trusted with the task regardless of how good the demo looked.
Escalation as a feature
Stopping and asking a person is a successful outcome, not a failure. Agents without an escape hatch produce confident nonsense.
Cost and latency
Each step costs money and time. A cheaper deterministic script often beats an agent for the same job.
We scope agents to a task with a measurable before-and-after. If we cannot state the metric, we recommend not building it.
How we deploy
From pilot to production.
Fit
Where an agent beats a plain workflow, and where it does not
Most things people want an agent for are better served by deterministic automation: cheaper, faster, auditable and incapable of inventing an answer. Agents earn their place on a narrower set of tasks, and being honest about that boundary is most of the value we add here.
Good: unstructured input
Reading an email, a document or a transcript and extracting what matters. Rules handle structure; language models handle mess.
Good: classification with nuance
Routing, prioritising and summarising where the categories are fuzzy and a human would also have to think about it.
Good: drafting
First drafts a person reviews — replies, summaries, briefs. The review step is what makes it safe, and it is not optional.
Bad: deterministic logic
If a rule can express it, a rule should. Rules are cheaper, faster, testable and they do not occasionally do something new.
Bad: anything irreversible unsupervised
Sending, deleting, charging, committing. Agents propose; people approve, unless the action is trivially reversible.
Bad: tasks needing exactness
Arithmetic, compliance checks, anything where being right ninety-five percent of the time is the same as being wrong.
We will frequently tell you that the thing you want an agent for is a workflow. It costs less, it breaks less, and you can explain what it did.
Guardrails
What we build around an agent before it touches anything real
An agent in a commercial process needs the same controls as a new employee with system access, and for the same reasons. These are not optional extras; they are the reason the thing can be allowed near your funnel at all.
Scoped permissions
Access to exactly what the task requires and nothing adjacent. The usual mistake is giving broad API access because it is simpler.
Human approval on consequence
Anything outward-facing or irreversible is proposed and held. The approval step is where most of the safety actually lives.
Full logging
Every input, decision and action recorded, so when something strange happens you can reconstruct it rather than speculate.
Explicit failure behaviour
What happens when the model is unsure, the API is down, or the input is unexpected. Defaulting to ‘do nothing and tell someone’ beats improvising.
Evaluation before and after launch
A test set of real cases with known right answers, re-run after every prompt or model change, because changes regress silently.
A visible off switch
Someone non-technical can stop it. If disabling an agent requires a developer, it will keep running through the incident.
The work
What an agent engagement actually contains
Narrow first, expanded only on evidence. We would rather ship one agent that works than four that need watching.
If the pilot shows the agent is not reliably better than the current process, we say so and stop. That is a successful outcome of a pilot, not a failed project.
Cost and ownership
What running an agent costs after it is built
The build is the small number. Agents in production carry ongoing costs that rarely appear in proposals, and knowing them in advance is the difference between a useful system and one that gets switched off in month five.
Inference spend
Per-call model costs that scale with volume. Modest at pilot scale and worth modelling honestly before you run it across a whole function.
Evaluation runs
The test set has to be re-run after every prompt change and every model update. This is the recurring cost that keeps the thing trustworthy.
Review time
Where a human approves outputs, that time is part of the economics. An agent saving ten minutes and costing eight in review saves two.
Drift maintenance
Providers update models without notice and behaviour shifts. Someone has to notice and correct, which means someone has to own it.
Integration upkeep
Every system the agent touches can change its API or its data shape. Ordinary software maintenance, and it applies here too.
The exit cost
What happens if you stop. Prompts, evaluation sets, logs and integration code stay with you, so the process can be rebuilt or reverted.
We put these in the proposal rather than discovering them together later, because an agent whose running cost exceeds its saving should not be built.
Honest limits
What we will not build
There is commercial pressure to put agents everywhere. These are the cases where we decline, and what we propose instead.
Unsupervised customer contact
An agent sending to customers without review will eventually say something you would not have said. Instead: drafting with approval.
Autonomous financial actions
Charging, refunding, committing spend. Instead: preparation and recommendation, with a person pressing the button.
Agents impersonating people
Automated outreach presented as a personal message from a named salesperson. Detectable, and it costs more trust than it earns.
Compliance decisions
Anywhere being wrong occasionally carries regulatory consequence. Instead: evidence gathering for a human decision.
Replacing a function wholesale
Agents handle tasks, not jobs. Projects framed as replacing a team reliably produce a mess and a rehire.
Anything unevaluable
If we cannot build a test set with known right answers, we cannot tell whether it works. Then we do not build it.
More AI Automation
The rest of the AI Automation stack.
Questions
AI Agents & Copilots, answered.
Is it safe to let agents touch our funnel?
Yes — every agent has guardrails and human checkpoints for anything customer-facing or high-stakes.
What can agents actually do?
Qualify inbound, answer product questions, enrich and route leads, draft follow-ups, and surface next steps — grounded in your data.
Is it safe to let an agent touch our funnel?
With the right boundaries, yes, and the boundaries are specific rather than reassuring generalities. Agents get narrowly scoped permissions, human approval on anything outward-facing or irreversible, complete logging, defined behaviour when they are unsure, and an off switch a non-technical person can reach. Inside those constraints they do useful work. Outside them — an agent with broad API access and no approval gate — we would not build it for you, and we would advise against anyone who offers to.
What can agents actually do well today?
Read unstructured input and extract what matters, classify and route where the categories are fuzzy, summarise long material, and draft text a person reviews. That covers a lot of real administrative work. What they do badly is anything requiring exactness, anything where a rule would be clearer, and anything with consequences that cannot be undone. We are specific about this because the gap between the marketing and the reliable capability is where most disappointing projects happen.
How do you stop it making things up?
Partly by constraining what it is asked to do — extraction and classification against supplied material hallucinate far less than open-ended generation — and partly by checking. Every agent has an evaluation set of real cases with known correct answers, re-run after every prompt or model change. Outputs that go to customers are reviewed by a person. Where a task cannot be evaluated this way, we take that as a signal not to automate it rather than as a reason to be careful.
Which models do you use?
Whichever fits the task, the latency requirement and your data constraints — and we design so that it can be changed, because the landscape moves faster than any engagement lasts. Where data residency or confidentiality rules out sending content to a third-party API, that narrows the options and we will say what it costs in capability. We do not have a vendor relationship steering the recommendation.
What happens when the model provider changes something?
It gets caught by the evaluation runs, which is precisely why they exist. Model updates change behaviour in ways that are invisible until something goes wrong in front of a customer — a prompt that worked reliably starts producing a different format, or an edge case that was handled stops being handled. Scheduled evaluation against the test set surfaces that within days. This is the main ongoing cost of running agents in production and it is not optional.
Can we start small?
That is the only way we will start. One task, one agent, a supervised pilot where a person reviews every action, and a decision after a few weeks based on whether it is actually better than the current process. Expanding comes from evidence, not from a roadmap written before anything was built. The pilot concluding ‘this is not worth automating’ is a good outcome — it cost weeks rather than a quarter.
How is this different from the workflow automation you sell?
Workflow automation is deterministic: defined triggers, defined steps, same input gives same output, fully auditable. Agents involve a language model making a judgement, which is powerful where the input is messy and unnecessary where it is not. Most real systems are mostly workflow with an agent handling the one step that genuinely needs language understanding. When we assess your tasks, the honest answer is usually that four-fifths of what you wanted an agent for should be a workflow.
What do you need from us?
Access to the systems involved, examples of the task being done well — which become the evaluation set — and a person who can judge whether an output is correct. That last one is the constraint that actually matters: without someone who can say reliably whether the agent got it right, there is no way to know whether it works, and we would rather not build something nobody can evaluate.
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