/ AI implementation
An agent that knows its job, uses your tools and asks before it acts. Built around how your business actually works.
Uppercut Labs builds custom AI agents for small and medium businesses across the UK and US. We connect a defined job to the information and tools needed to do it: a CRM, an inbox, a document library or a team chat. The result belongs inside the working day, rather than in another app nobody remembers to open.
An agent can choose between permitted steps: look up a record, ask for missing information, prepare a draft or request approval. That flexibility is useful when the route varies. It also needs boundaries. We agree what the agent can read, what it can change and when a person must take over before implementation starts.
A custom build is not always the right answer. If an existing assistant or a straightforward automation can do the job reliably, we recommend that first. The opportunity sprint tests the business case before you commit to an agent.
Example scopes to discuss, not claims of client results. Each needs access checks and a clear owner.
Your team spends time finding the latest policy, project decision or customer detail.
An assistant in Teams or Slack searches approved company sources and returns an answer with references. The implementation respects the intended audience and source access.
An answer without adequate supporting material should say so or escalate. Connecting documents does not make every answer correct.
Enquiries, meeting notes and customer updates arrive in different places.
The agent finds the customer record, extracts the relevant details and prepares a CRM update or follow-up for review. Missing fields become a question instead of a guess.
Sending messages, changing deal stages and making commitments use the approval policy agreed with your team.
A routine request still needs someone to gather context from several systems.
An operator checks approved records, prepares a task list or status summary and routes exceptions to the right person. Start with one recurring operational job.
Tool permissions, spend limits and a manual fallback constrain the work. Untrusted emails and documents are data, not instructions to the agent.
Best for a recurring job that requires context and a choice of actions, with enough volume to justify integration and a clear owner who can review the result.
Use an ordinary workflow when the steps are predictable. Use a supported off-the-shelf assistant when its existing integrations and permissions meet the need. Avoid broad autonomy when you cannot define what success or failure looks like.
Define the user, task volume, current effort and what a useful outcome looks like. Review a sample of real requests, including awkward ones.
Agree source access, tool permissions, approval steps and retention requirements. Integrations depend on the APIs and account permissions actually available.
Check representative cases, missing information, incorrect instructions and tool failures. Agree acceptance criteria with the person responsible for the process.
Roll out to a small group, train the team and watch the exceptions. Review quality, usage and cost before widening the scope.
Yes, subject to the workspace permissions and integration options available. We check app approval, identity, data access and the actions the agent needs before choosing the implementation. A chat interface is only the front end; the useful work is in the connected process.
A chatbot primarily exchanges messages. An agent can also select and use approved tools to complete a defined job. Some useful systems combine a simple chat interface with a deterministic workflow. We choose the least complicated approach that satisfies the task.
Use approved sources, require references where appropriate, restrict tool access and test real and adversarial cases. Consequential actions need agreed approval. These controls reduce risk; no language model can be promised to never make a mistake.
Often. We first review its architecture, source access, tool permissions, logs and known failures. Taking over an existing deployment may require remediation before an ongoing support scope can be agreed.