Claude-Powered AI agent development
for your business.

Common use cases include: qualifying and prioritising sales leads, generating daily business intelligence reports, summarising and routing customer support tickets, monitoring systems and sending alerts, conducting market research, and analysing financial data. If a task involves reading information, reasoning about it, and taking an action-a Claude agent can likely automate it.

Yes. We build tool integrations as part of the agent architecture. Claude agents can connect to CRMs, databases, Slack, email systems, ERPs, and any platform that exposes an API. We handle the integration design, authentication, and error handling as part of the build.

Claude consistently performs well on tasks requiring careful instruction-following, multi-step reasoning, and document analysis-which are core to most business agent use cases. It also has strong safety characteristics and a large context window, which matters when agents need to process long reports or datasets in a single pass.

PixlerLab is one, and our work is documented rather than asserted — FlyCRM runs sixteen AI surfaces for a staffing agency, Opshire scores every role on six weighted signals and suppresses 96% of the candidate set, and an eight-agent content pipeline has run a technical blog unattended since February 2026. When you evaluate anyone for this work, ask three things: what have you shipped that is live right now, how do you stop the model writing to production, and can I read a case study with real numbers in it.

This is the bulk of what we do — agents that pull from every system on a schedule, reconcile the numbers, and hand back structured findings with the reasoning attached. The part that decides whether it is safe to deploy is tenant isolation. FlyCRM enforces it with 236 row-level security policies at the database, so what an agent can read is settled by Postgres rather than by the prompt. Ask any vendor how they scope what the model can see; if the answer is that they tell it not to look, keep looking.

We build every agent this way by default. The agent proposes, a person confirms, and only then does anything commit — zero records written by a model without a human confirm on FlyCRM, zero applications sent without the candidate's click on Opshire, and every rupee that moves at Cashflo behind a human decision. It is a boundary you have to build deliberately: the write has to be gated, logged and reversible, and an approval that fires automatically is not an approval.

We do, though it is worth being precise about what we have shipped. Our operations work is agents that watch queues, thresholds and inboxes continuously, triage whatever trips, and escalate with context instead of a bare alert — the pattern behind the three-minute response SLA on our GTM agent build, and behind the 70% cut in manual operations work at Cashflo. We have not yet shipped onto a plant floor or into an MES, so if your project depends on that specific experience, ask us and we will tell you straight.