AI that keeps working after your team goes home

AI Agents & Automation

Voice and chat agents, plus automations that handle the repetitive work around the clock, so your team spends its time on what actually needs a person.

Agents that do the work, not just talk about it.

Autosteps
Humanon approval
Trailfull

job card · objective to donesupervised

Read & triagedone

Take actiondone

Escalate hard caseto human

Grounded, guardrailed, audited

1 of 3 went back to a person. That is the setting, not the failure.

24/7

Always on

In your tools

Real actions

Supervised

Guardrails

Auditable

Every step

Beyond chat

The point isn't a smarter chatbot. It's work getting done.

An autonomous agent takes an objective and owns it: it plans the steps, uses your systems to act, and pushes the task through to done, calling in a person only when it has to.

It runs inside guardrails, with approvals and a full audit trail, so you get the efficiency of automation without giving up control.

Runs the whole task

A bot answers one question. A worker carries an objective all the way to done.

Works inside your tools

Connected to your systems, it reads, acts, and updates the tools your team already uses.

Supervised, not unleashed

Clear guardrails, approval steps, and a full audit trail. Autonomy, with you in control.

7 jobs you can hand over

AI Voice Agents

Phone agents that answer every call, book appointments, and take orders, day or night.

AI Chatbots & Lead Qualification Agents

Chat agents that greet visitors, qualify leads, and hand the warm ones to your team.

Workflow & Business Process Automation

Connect your CRM, inbox, and tools, and let the repetitive work run itself.

Private RAG Systems / Internal Knowledge AI

AI assistants that answer from your own documents and data, kept private.

Multi-Agent AI Systems

Teams of AI agents that coordinate on complex, multi-step operations.

Managed AI Agents (Monthly)

We run, monitor, and keep improving your agents on a monthly plan.

Autonomous AI Workers

AI workers that own a standing queue of work and push it toward a goal, pausing for approval before anything consequential.

What changes once it is running

The difference shows up in operations, on the Monday after launch. Here's what's true once it's live.

01Work finishes instead of queuingAn objective handed over comes back done, rather than sitting in an inbox waiting for someone to find a free hour.

02Your systems stay the recordThe agent acts inside the tools you already run, so nothing has to be re-keyed into them afterwards.

03Consequential steps still need a personApproval gates sit on the actions that cost money or reach a customer. Everything either side of them runs on its own.

04You can see exactly what happenedEvery step is on the trail in order, with what the agent read and what it changed.

05Cover outside office hoursOvernight and weekend work is picked up as it arrives instead of stacking up for Monday morning.

06Autonomy widens as it is earnedStart narrow and raise the limit as the trail proves reliable, the same way you would onboard a person.

Where this lands hardest

The four situations this practice is asked for most often, and what it is actually doing in each.

01Support and service desksFirst-line triage, classification, and resolution of the tickets that follow a known path, with the rest escalated with context attached.

02Finance and back officeReconciliation, data entry, and chasing: the multi-step work that spans three systems and belongs to nobody.

03Sales operationsEnrichment, routing, and follow-up kept current without a person copying fields between tools.

04Field and scheduling teamsJob intake and dispatch handled as calls and forms arrive, including the hours nobody is staffed for.

Work handed over, and done

Restaurants & Hospitality

AI phone ordering that fills every table's takeout slot

Home & Field Services

A dispatch platform that keeps crews moving

Travel & Tourism

The invoice and the trip it pays for were separate records, so nobody could say which trips were unpaid

What it connects to

It works inside the stack you already run. These are the connections this practice is built against most often.

01CRMReads and updates records, so the work lands against the right account rather than in a separate log.

02Email and shared inboxesTriage, drafting, and reply on the inboxes your team already watches.

03Calendars and schedulingReal availability, holds, and confirmations rather than a suggested time to check later.

04Ticketing and helpdeskOpens, classifies, and closes the tickets that follow a path you can describe.

05Your own databasesAnswers come from your records rather than the open web, with access scoped per agent.

06Approval channelsChat and email approvals routed to the person who actually owns the decision.

Built to run in production

We build agents on proven models and frameworks, ground them in your data, put guardrails around them, and wire them into the tools your team already uses. That's what makes them hold up outside the demo.

Grounded in your own data (RAG), never the open web
Human-in-the-loop and escalation by design
Connected to your calendar, CRM, and systems
Monitored and tuned after launch

How an engagement runs

Four stages, in order, and exactly what happens in each.

01Scope the objectiveWeek 1We pick work well-defined enough to automate confidently, and write down where the agent's authority stops.

02Build inside your stackWeeks 2-3The agent is grounded in your data and wired to the tools it needs to read and act in, with guardrails and approval gates in place.

03Supervised runWeek 4It works real cases with a person watching the trail, and we tune against what it actually did rather than what we expected.

04Raise the limitOngoingAs the audit trail proves reliable, autonomy widens onto the next slice of work instead of the guardrails coming off.

The authorised limit

Autonomy with control, not instead of it

Every agent runs inside defined guardrails, with human approval on the actions that matter and a full audit trail. Autonomy is earned as the agent proves itself. The limit goes up; it never disappears.

6 things you end up with

Each one is something you can point to when the engagement ends. The list is deliberately short: an agent that completes real work needs connections, limits, a record of what it did, and a way to hand off to a person. None of it is optional.

01AI agents that complete real tasks
02Connections to your tools and data
03Guardrails and human-approval checkpoints
04A full audit trail of every action
05Monitoring and clean human handoff
06A path to widen autonomy as trust grows

Why bring this to us

Six commitments, each one something we actually do differently.

01We automate work, not conversationsThe measure is whether the task reached done, not how polished the reply sounded.

02We ground agents in your dataRetrieval over your own records rather than the open web, so answers are ones you can stand behind.

03We design the escalation firstWhat an agent may not do is agreed before it does anything, so nothing surprising is discovered in production.

04We build on proven modelsNo bespoke model where a well-chosen one and good engineering will do the job more reliably.

05We monitor after launchAgents drift as your processes change. Ours are watched and tuned, never shipped and forgotten.

06We show the trailEvery action is inspectable, which is the only honest basis for widening what an agent is allowed to do.

What people ask before they hand work over

How is this different from a chatbot?

A chatbot answers questions. An autonomous agent takes an objective and does the work: it plans the steps, uses your tools, and carries the task through, calling in a person only when it needs to.

Isn't letting AI act on its own risky?

It would be, without controls. That's why every agent runs inside defined guardrails, with human approval on the actions that matter and a full audit trail. Autonomy is earned step by step, as the agent proves itself.

What kind of work suits an AI agent?

Repetitive, rules-heavy tasks that span several steps and systems: triage, data entry and reconciliation, routine research, first-line support. We start with work that is well-defined enough to automate with confidence.

Will it work with our existing systems?

Yes. We connect agents to the apps and data you already use, so they work inside your stack instead of becoming one more disconnected tool.

The other eight practices

One senior team across the whole lifecycle, so the neighboring practice is a colleague, never another procurement exercise.

01AI Integration & Custom AI Development6 svc

02AI Transformation & Consulting7 svc

03Mobile & Web Applications8 svc

04Enterprise & B2B Platforms7 svc

05Product Design & UX10 svc

06Data & Analytics Engineering5 svc

07Managed Services & Support14 svc

08Marketing & Strategy10 svc

Have a project?

Let's talk

Running a large platform, shaping a first MVP, or getting a product ready for a funding round? Tell us where you are. We'll shape the process around it, and stay with you after launch.

or email hello@flaidex.com