AI SDR and BDR agents
An agent researches every inbound lead, enriches and scores it against your ICP, writes a genuinely personalised first touch and books qualified meetings into the calendar. How it works →
AI Agent Development
A custom AI agent is given a goal, a set of tools and hard limits, then decides its own steps to reach the result. We design, build and operate them — with the guardrails that make handing software real responsibility a sane decision.
"AI agent" has been stretched to mean almost anything, so here is the working definition we build to: an agent receives a goal rather than a script, chooses which tools to use and in what order, observes the result, and adapts. A chatbot answers. An agent acts — it books the meeting, moves the budget, updates the record.
That autonomy is genuinely useful for problems where the right sequence can't be known in advance. It is also the reason agent projects fail more often than ordinary automation: without limits, an agent that chooses wrong chooses wrong at machine speed. Our entire build practice is organised around that trade.
Each runs against your real systems, inside limits you set.
An agent researches every inbound lead, enriches and scores it against your ICP, writes a genuinely personalised first touch and books qualified meetings into the calendar. How it works →
Strategy, distribution and performance agents cooperating on live ad accounts — reallocating budget toward what converts, inside hard spend ceilings. How it works →
Agents that read the account, take the action the customer asked for, and escalate anything ambiguous with the full history attached. How it works →
Continuous competitor, market or inventory watching that produces briefs and alerts — and knows the difference between a blip and a trend.
Agents that own a recurring internal outcome — the inbox at zero, the pipeline data clean, the reports out — rather than a single task.
Several specialised agents with one coordinator, for work that spans functions. Each has its own tools, limits and audit trail.
Anyone can prompt a model into acting. Making it safe to let one act unattended is the actual work.
An agent can only use the tools you grant, scoped to exactly what its job requires — never blanket account access.
Spend ceilings, change caps and rate limits the agent cannot cross, enforced outside the model where it can't be talked around.
Irreversible or expensive actions stop and wait for a human — one tap, with full context. Our framework →
Every decision logged with its reasoning, so "why did it do that?" always has an answer.
Often not — and we'll say so. If a competent temp could follow a checklist to do the job, a fixed workflow is cheaper, more predictable and easier to audit. Agents earn their complexity when the environment keeps changing and the right sequence genuinely can't be written down in advance.
Most engagements end up as a mix: rails for the predictable spine of the process, an agent for the part that needs to adapt. We scope that split on the first call, and we don't upsell autonomy you don't need.
Deeper reading on how this works in practice.
Book a free call. We'll tell you honestly whether your problem needs an agent or a simpler workflow — and what each would cost.
Book a free call