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Put the repetitive half of your operation on rails.

AI copilots, voice and calling agents, document intelligence, workflow automation and predictive systems.

Service AI & Intelligent Automation

01 — Problems

You are probably here
because of one of these.

  • A team spending hours a day re-keying data between systems that do not talk to each other.
  • Inbound calls or enquiries going unanswered outside business hours.
  • Invoices, forms and documents being read and typed up by a person.
  • Support answering the same forty questions every week.
  • Decisions made on a spreadsheet exported last Tuesday.

02 — Capability

What we actually do.

How we picture it: A node network in which manual steps collapse into automated flows.

  • AI copilots embedded in your existing product
  • Voice and calling agents for qualification and booking
  • Document intelligence — OCR, extraction, validation
  • Workflow automation across CRM, WhatsApp and email
  • Predictive models for demand and lead scoring
  • Retrieval systems over your own documents

03 — Scope

What is in, and what is not.

The right-hand column is the one most studios leave out. Naming the exclusions up front is how a fixed price stays fixed.

Deliverables

  • Automation map of the current manual process
  • Working agent or model integrated with your systems
  • Evaluation set and accuracy baseline before go-live
  • Human-in-the-loop escalation path
  • Prompt, model and cost documentation
  • Deployment on infrastructure in your own account

Not included

  • Training a foundation model from scratch
  • Ongoing model API costs — those are billed to your provider account, not marked up through us
  • Data labelling at volume, unless quoted separately
  • Accuracy guarantees on data we have not seen

05 — Technical approach

What we build it with.

Chosen per project against your constraints — not a fixed house stack, and not a logo wall. The test is what your team can maintain after we hand it over.

Models

  • Claude
  • OpenAI
  • Open-weight models where privacy requires it

Voice

  • Speech-to-text
  • Text-to-speech
  • Telephony integration

Document

  • OCR pipelines
  • Structured extraction
  • Validation rules

Delivery

  • Firebase
  • Supabase
  • AWS
  • WhatsApp Business API

How this gets proved

  • Every automation ships with an evaluation set, so accuracy is a measured number rather than an impression.
  • Where data cannot leave your environment, the deployment model is chosen to match — on-device or in your own cloud.
  • Nest Voice AI and our document-processing work are both operating systems, not demos.

06 — Delivery

Checkpoints and timelines.

You sign off at each of these

  1. Discovery You approve the automation map and the fixed price
  2. Baseline You approve the accuracy baseline before go-live
  3. Pilot You run it on real work with a human in the loop
  4. Launch You sign off, and the accounts transfer to you

Typical timelines

Single automation or agent
2–4 weeks
Document intelligence pipeline
4–8 weeks
Multi-channel AI platform
8–16 weeks

Ranges, not promises. Your actual timeline is fixed in writing in the proposal once the scope is agreed.

08 — Questions

The ones people actually ask.

Only if you decide it should. Where confidentiality or regulation requires it, we deploy open-weight models inside your own environment instead. We tell you which architecture we are proposing, and why, before you approve the scope.

Project qualification

Get a fixed-price proposal.

Tell us the problem, the stage you are at and the budget band. You get a discovery call, then a written proposal with exact scope, milestones and a final number — within 24 hours.

Response
Fixed-price proposal within 24 hours
Confidentiality
Mutual NDA signed before any project detail is discussed

Prefer to just talk? WhatsApp is answered fastest during studio hours.