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Gezora.ai

Bespoke AI agents for your workflows

Gezora builds bespoke AI agents around the specific workflows other tools cannot cover, then trains your team to run them and scales as results prove out.

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Stages and safeguards in the build

  • Workflow discovery
  • Decision mapping
  • Custom prompts
  • Agent logic
  • Approval guardrails
  • Audit logging

What goes into a bespoke agent

  • Workflow discovery and mapping
  • Custom agent logic and guardrails
  • Secure integrations into your systems
  • Staged rollout and handover

80%

less manual effort once running

4

step process, from discovery workshop to launch

3

stage rollout, from prototype to production

Approximate results from Gezora's own deployments, not a guarantee.

Why the agent fits your workflow

  • Built for your workflow

    The agent is designed around the workflow you already run, not around a generic template.

  • Guardrails in the first build

    Approval steps and audit logging ship with the first version rather than being retrofitted later.

  • Connected to your systems

    Secure integrations let the agent work inside the systems your team already uses.

  • The repetitive steps move off your team

    Repetitive steps move to the agent so your team spends its time on judgement work.

Stages and safeguards in the build

  • Workflow discovery
  • Decision mapping
  • Custom prompts
  • Agent logic
  • Approval guardrails
  • Audit logging
  • Secure integrations
  • Prototype stage
  • Pilot stage
  • Production rollout
  • Operator training
  • Runbook handover
  • Optimize and scale

What goes into a bespoke agent

Each agent is scoped to one workflow, built around its real steps, and rolled out in stages until it holds up in production.

  • Workflow discovery and mapping

    The build opens by mapping how the workflow runs today, who touches it, and where the decisions sit. What the agent should own comes out of that mapping rather than being assumed at the start.

  • Custom agent logic and guardrails

    The agent runs on prompts and logic written for your process, with approval guardrails on the steps that need a person. Audit logging is part of the same build, so every action stays reviewable.

  • Secure integrations into your systems

    The agent reads and writes through authenticated connections into the systems the work already lives in, so records stay where your team expects them. Nothing has to move into a separate tool for the agent to be useful.

  • Staged rollout and handover

    The agent moves through prototype, pilot, and production, so nothing reaches live use before it has been proven at the stage below. Documentation is handed over at the end and the agent keeps being optimized after launch.

From workshop to production

To start we need a describable workflow, system access, a process owner, and representative data.

  1. 01

    Step 1, Discovery workshop

    A workshop maps the workflow end to end with the person who owns the process.

  2. 02

    Step 2, Prototype and refine

    A prototype runs on your process as a pilot while prompts, logic, and guardrails are tuned.

  3. 03

    Step 3, Secure deployment

    Integrations go live in production with approval guardrails and audit logging in place.

  4. 04

    Step 4, Optimize and scale

    Documentation is handed over and the agent keeps improving as it takes on more work.

Who a bespoke agent is for

This is for the workflow that never fitted a product you could buy, and still runs on people moving work between systems by hand.

  • Teams whose workflow no tool covers

    The process is specific enough that bought software only handles part of it, so the rest stays manual. The agent is designed around the workflow you already run instead of a generic template.

  • Teams whose repetitive steps sit inside one specialist workflow

    The same steps get repeated every day, and the judgement work queues up behind them. Those steps move to the agent so the team spends its time on the decisions that need a person.

  • Teams with no one in house to write agent logic

    The build keeps being scoped and never reaches the top of an engineering queue. We build it, hand over the documentation, and train the people who will run it.

  • Teams whose first agent has to survive scrutiny

    Nobody wants to find out in live use whether an agent holds up. The agent moves through prototype, pilot, and production, so nothing reaches live use before it has held up at the stage below.

What we hand over

The engagement hands over a working agent and everything your team needs to run it without us in the room.

  • The workflow map the agent was scoped against
  • The guardrail set showing which steps stop for a person
  • Custom prompts and agent logic for your process
  • Authenticated integrations into your live systems
  • Approval guardrails and audit logging in production
  • Operator runbooks and a handover training session

Your team runs the agent from handover, and we keep optimizing it as it takes on more work.

FAQ about Enterprise AI Agents

Straight answers on scope, timelines, and what running Enterprise AI Agents asks of your team.

Other work that pairs with this

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Stop paying people to do what an agent can

Tell us what you want to automate. We will map the workflow, deploy the right agents, and train your team to run them.

  • Every agent is trained on your own workflows, never a generic template
  • Most deployments are live within two to four weeks
  • SOC 2 compliant, with a complete audit trail on every deployment
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