What we build
Custom AI agents built for real work.
We design and build AI agents that answer questions, use your tools, process requests, draft work, trigger workflows and escalate safely when a human should take over.
The problem
A chatbot is easy. A useful agent is a system.
Most AI demos stop at a chat box. Real business agents need context, permissions, tools, memory, evaluation, logging and fallback behavior. Without those pieces, the agent sounds impressive and then fails the moment it touches live operations.
- The assistant is not connected to the tools where work actually happens.
- Answers are not grounded in company data, so confidence can outrun accuracy.
- There is no approval path for actions that affect customers, money or records.
- Nobody can measure whether the next model prompt is better than the last one.
What we build
The pieces, built around your stack.
Some pieces belong in no-code tools. Some need custom code. Some need AI. We choose based on reliability, speed, ownership and the actual workflow.
Support & knowledge agents
Agents grounded in your docs, policies, SOPs and data, with citations and escalation rules for anything uncertain or high-stakes.
Sales & marketing agents
Agents that qualify leads, draft replies, summarize calls, prepare follow-ups and update the CRM with clear human approval points.
Operations agents
Agents that read requests, check systems, prepare records, trigger workflows and hand off exceptions with the full context attached.
Guardrails, evals & logs
Structured outputs, allowed actions, test sets, review queues, audit logs and fallback paths so the agent can be improved safely.
The pipeline
Not just a prompt. An agent with a job.
We define the task, connect the tools, constrain the actions, test the behavior and ship with monitoring.
Map your workflow→Define
We choose the agent's job, boundaries, data sources, allowed actions and the moments where a human must approve.
blueprintGround
The agent gets retrieval, examples, SOPs, structured data and tool access, so answers and actions come from your operation.
buildConstrain
Outputs follow schemas, actions require permissions, and risky steps route through review instead of happening automatically.
guardrailsEvaluate
We build tests from real examples so improvements are measured, regressions are caught and prompt changes are not guesswork.
every changeOperate
Logs, dashboards and review queues show what the agent did, why it did it and where it needs tuning.
ongoingBest for teams that need AI to do more than chat: answer, draft, decide, route, update and escalate.
Straight answers
Questions we get about this.
We choose based on the task and can work with OpenAI, Anthropic and other providers. The agent is designed so the model can be swapped when a better fit appears.
Yes. It can create records, update CRM fields, send drafts for approval, trigger workflows, search databases or call APIs. We define permissions and approval rules before it touches anything important.
We ground the agent in your data, constrain outputs, validate tool calls, add review for risky actions and test behavior with evals. The goal is managed reliability, not pretending AI has zero error.
Automation audit · free
Build the automation your team keeps describing.
Free, 20 minutes. We map your workflow, identify where AI and automation fit, and tell you whether no-code or custom code is the right path.
No deck. No pressure. A practical automation map you keep either way.