AI Agents & Automation
Grapefruit designs and builds AI agents that complete real steps in your business workflows, such as triaging requests, drafting replies, updating your CRM or preparing reports. Agents run on your systems with clear permissions, measured accuracy and a human approving the steps that matter, and we keep improving them after launch.
Which steps an agent does, which a person approves, and which stay manual, mapped on your real process and volumes.
Secure connections to your CRM, ticketing, ERP, email or document stores, with least-privilege permissions per action.
A test set of real cases, success metrics, limits on what the agent may do, and escalation to a human when confidence is low.
Every action logged and reviewable, with dashboards for accuracy, volume, cost and escalations.
Documentation, prompts, evaluation sets and training for the people who work alongside the agent.
Our method
Map the workflow, volumes, exceptions and systems. Pick the steps where an agent saves the most time with acceptable risk.
Build a working agent in your tenant on a narrow slice of the workflow and measure it against real cases with the team that will use it.
Harden integrations, add guardrails, human approval steps and monitoring, then roll out to more volume and more steps.
Re-run evaluations when models or processes change, tune cost and accuracy, and review new automation candidates each quarter.
Proof
We have not published a client case study for this capability yet. Here is the AI work we have shipped and can walk you through on a call.
Ways to start
A working AI prototype on your own data in 4 weeks, so you decide with evidence, not slides.
Find the 3 AI use cases worth funding in your business, with the numbers to defend them.
From validated prototype to a production AI system your team trusts and runs.
Your AI keeps working, stays compliant and keeps improving.
Industries
FAQ
Rule-based automation follows fixed steps. A chatbot answers questions. An AI agent can decide which step to take next and use tools, such as looking up an order or drafting a reply, within the permissions you give it. Most useful business agents combine all three, with a human approving sensitive actions.
Each agent gets least-privilege access to specific actions, hard limits on what it can change, and approval steps for anything costly or customer-facing. Every action is logged. We test these limits on real cases during the pilot, before the agent touches production volume.
Agents run in your cloud tenant or an EU region we agree on, connect to your systems with scoped credentials, and your data is not used to train public models. A data processing agreement covers our work and the data flows are documented for your security team.
No. We choose the model per use case and keep it behind an abstraction layer, so it can be swapped when a better or cheaper model appears. You own the code, prompts, evaluation sets and documentation.
It depends on what the agent does. Most workflow agents are not high-risk, but transparency duties under Article 50 have applied since 2 August 2026 when people interact with AI or receive AI-generated content. We classify each agent at the start and document the controls.
The number of systems it must integrate with, how many exceptions the workflow has, how sensitive the data is, and how much human approval is needed. Usage costs for models are usually small next to integration work. We estimate after a prototype, when the numbers are measured rather than guessed.
A working prototype takes 3 to 4 weeks. Taking it to production with integrations, guardrails and monitoring usually takes another 2 to 4 months, depending on your systems and approval processes.
Related
A working AI prototype on your own data in 4 weeks, so you decide with evidence, not slides. 3 to 4 weeks, fixed scope, no obligation to continue.
We reply within 1 business day, usually the same day. Not ready to talk? Take the free AI assessment.
