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Geeks & NomadsAI for Businesses
AI for businesses · Built and operated in production

AI products and platforms, engineered to run in production.

Geeks & Nomads is an applied AI engineering firm. We build AI agents, custom AI software and the digital platforms and integrations underneath them — then operate them: accuracy monitored, models migrated, running costs held under a ceiling you approve.

CHANNELSTHE AI LAYER WE ENGINEERYOUR SYSTEMSWhatsAppBUSINESS APIVoiceINBOUND + OUTWeb & appCHAT + FORMSAI layerRetrieval over your dataTool use on your systemsGuardrails + refusalEvals in CICRM / ERPREAD + WRITEYour documentsCITED, NOT GUESSEDHuman approval gateSIZED TO THE RISKOnly where being wrong is expensiveRUNNING UNDERNEATH, EVERY DAYAccuracyDriftCost ceilingModel migration
ClaudeGPTGeminiLlamaWhatsApp Business APINext.jsPostgresn8nTwilioSalesforceHubSpotZohoShopifyVercelSupabaseMeta Ads APIGoogle Ads APIFreshdeskClaudeGPTGeminiLlamaWhatsApp Business APINext.jsPostgresn8nTwilioSalesforceHubSpotZohoShopifyVercelSupabaseMeta Ads APIGoogle Ads APIFreshdesk

Services

Five services. Engineered, then operated.

Four of them are engineering. The fifth is the demand layer that makes sure somebody arrives at what we built. Each has a defined scope, a timeline and a number it is meant to move.

012–3 weeks to production

AI Agent Development

WhatsApp, voice and web agents that answer, qualify and route every enquiry in seconds.

  • WhatsApp AI agent development
  • AI voice agent development
  • Website chat and support agents
  • Agent orchestration and tool use
  • CRM, ERP and helpdesk integration
  • +1 more
CTO · CMO · Head of Sales · Customer ExperienceExplore
024–6 weeks to production

Custom AI Software Development

LLM applications, agentic workflows and AI features engineered into your own product.

  • LLM application development
  • RAG and knowledge systems
  • Agentic workflow automation
  • Document and data extraction
  • AI features inside your product
  • +1 more
CTO · VP Engineering · Head of ProductExplore
033–5 weeks depending on scope

Digital Platform Engineering

Websites, commerce, portals and web applications engineered for speed, conversion and AI discoverability.

  • Website design and development
  • Ecommerce development
  • Web and mobile application development
  • Systems integration and data engineering
  • AI-native content architecture
  • +1 more
CTO · Head of Digital · CMOExplore
041–2 weeks

AI Consulting & Enablement

Where AI pays in your business — costed, sequenced, governed, and taught to your team.

  • AI readiness and data assessment
  • Use-case identification and ROI modelling
  • AI licence and adoption audit
  • AI governance, policy and risk
  • Architecture and build-vs-buy review
  • +1 more
CTO · COO · Head of Transformation · CMOExplore
05First campaigns live in week one

Growth Marketing & Search

The demand layer on top of what we build — performance media, SEO, AI search visibility and content, run as one instrumented system.

  • Performance marketing
  • AI search visibility (GEO)
  • Search engine optimisation
  • AI content production
  • Lifecycle and CRM automation
  • +1 more
CMO · Head of GrowthExplore

Where every engagement starts

We work out which of them you actually need. Free.

A 30-minute call, and a comprehensive written engineering plan within 24 hours: what to build, in what order, the architecture, and what it costs to run. Yours to keep.

How the diagnostic works
01

A 30-minute call

Online, this week

Not a questionnaire and not a pitch. Thirty focused minutes on how the work actually flows — where enquiries land, which systems touch what, where time disappears, what breaks, and what it costs you when it does.

02

We do the work the same day

Straight after the call

We run your AI visibility scan, map every candidate against your stack and your data, and cost each one properly — including the ones we are going to tell you not to build.

03

A comprehensive plan, within 24 hours

24 hours from the call

What to build, in what order, the architecture we would use, what it would take, what it is worth, and what we would start with on Monday. In writing, yours to keep — including if you act on it with someone else or with your own engineers.

04

If it makes sense, we build it

From there

This is the part most consultants do not do. We are the same people who wrote the plan, so nothing is lost in a handover — and we run it in production afterwards.

How we work

Principal-led, from architecture to production.

Every engagement is principal-led. The people who architect your system are the people who build it, and the people accountable for it once it is carrying real traffic.

BUILDRUN — WHERE THE VALUE IS ACTUALLY DECIDED2–6 weeksThe next 18 months and beyondMOST FIRMS HAND OVER HEREAccuracy driftsProviders deprecate modelsCosts creepContent goes staleWe are still here: monitoring, eval regressions, model migration, cost ceiling, monthly report.
01

Principal-led, end to end

No pitch team handing you to a delivery team. The person in your first meeting owns the architecture, the build and the production behaviour — and is reachable when something is on fire.

02

We work to your engineering standard

Your repository, your code review, your release process, your definition of done. Where you have no standard, we bring ours: eval gates in CI, tracing, audit logging and a documented architecture.

03

Strategy and delivery in one firm

Consultancies hand over a deck. Integrators build whatever the deck said. We are accountable for both halves, which is why we are careful about what goes into the plan in the first place.

04

We own it in production

Accuracy monitoring, eval regressions, model migrations, cost ceilings and a monthly report. The value of an AI system is decided in the eighteen months after launch, and that is the part most firms are not there for.

6

Model providers our own platform orchestrates in production

Claude · GPT · Gemini · Perplexity · Copilot · Grok

2–3 wks

From first call to an AI system running in production

We build with agentic tooling. Weeks, not quarters.

0%

Markup on model and infrastructure costs

Billed at cost against a ceiling you approve

24/7

Accuracy, drift and spend monitoring on everything we run

With a measured number in your inbox every month

For enterprise buyers

Enterprise-ready before you ask.

AI work stalls inside large organisations for reasons that are almost never technical. Residency, accuracy governance, oversight, cost control and commercial terms are designed in from the first architecture session — not retrofitted when your security review sends it back.

01

Data residency and security

Regional endpoints, deployment inside your own environment, open-weight models hosted by you where policy requires it, PII redaction before any external call, and full audit logging. Written for your security function to review properly rather than to be waved past them.

02

Accuracy you can govern

Every AI system is tested against a labelled set drawn from your own data, with pass thresholds agreed in writing before go-live and measured continuously afterwards. You get a number every month, not an assurance.

03

Human oversight by design

Approval gates, escalation paths and audit trails sized to the consequence of an error — not to how impressive the demonstration looks. Where being wrong is expensive, a person signs off before anything happens.

04

Cost governance

Model and infrastructure costs billed at cost against a monthly ceiling you approve. We never mark up tokens, because a margin on usage would corrupt every architectural decision we make on your behalf.

05

Commercial and procurement

MSA, data processing agreement, defined SLAs, named accountable lead, fixed scope agreed before work starts, and GST-compliant invoicing from an Indian entity. No hourly billing and no scope-creep invoices.

06

Executive reporting

Monthly reporting written for the person who has to defend the budget — reconciled numbers, contribution by channel, and a clear recommendation rather than a dashboard screenshot.

Why us

Why this works when the last attempt did not.

Most AI projects are fine until the demo ends. Then the model underneath changes, accuracy drifts, costs creep, and nobody is left watching. Every point below is something we do about that — and every one is checkable.

01

We ship production AI — including our own

GenAI Ranker is our product: multi-tenant, six model providers orchestrated, row-level tenant isolation, live billing and real incident nights. Most firms selling AI can show you a prototype. We can show you a production system we own, operate and are paged for — and you can watch us open it in the meeting.

02

We build with agentic AI — fast, and still disciplined

Our own delivery runs on agentic tooling, which is why an agent is live in days rather than a quarter. The speed does not come out of the quality budget: eval suites, regression gates, tracing, guardrails and cost ceilings are still there, because that is what makes it survive contact with real users.

03

We argue about the business case, not just the architecture

We cost the current process before proposing a system, and we can defend the build to a finance committee as well as to an architecture review. Most AI vendors are fluent in one of those conversations. The one your board will actually hold is usually the other.

04

We never mark up model or infrastructure costs

Billed at cost against a ceiling you approve. A margin on tokens would corrupt every architectural decision we make on your behalf — model choice, caching, routing, context size, all of it.

05

We build the demand layer too

A platform nobody arrives at and an agent nobody talks to are worth nothing. We run the search, content and campaigns that feed what we build, instrumented end to end — so the system and the demand for it are designed together.

06

We keep it running

Monitoring, retraining, eval regressions, model migrations and cost control after launch. Most firms hand over and vanish, right before the model underneath gets deprecated. That gap is the whole reason clients stay with us.

In the agreement

Things you can hold us to.

Every one of these is written into the contract, not asserted on a website. They are the terms we are comfortable being held to, and the ones most firms will not put in writing.

01

A fixed price before we start

Setup fee, monthly fee and timeline agreed in writing. No hourly billing, no scope creep invoices, no surprises at the end.

02

An accuracy number, not a promise

We test against your real examples before go-live and agree what "working" means. You get a measured figure every month, not reassurance.

03

Model costs at cost, always

We never mark up tokens or infrastructure. A margin on model usage would make us want you to use more of it.

04

A running-cost ceiling

Agreed up front. If we cross it, that is ours to engineer around — not a surprise line on your invoice.

05

We will tell you not to buy something

If an automation will not pay for itself, or your real problem is not the one you asked us about, we say so before you spend.

06

A named person who answers

Not a ticket queue with a first-response SLA. Someone who knows your account and picks up.

Straight answers

Questions we get before the first call.

Written plainly, so a person and an AI engine can both quote them accurately.

What does Geeks & Nomads do?

We are an applied AI engineering firm. We study how a business actually runs, produce a free AI Opportunity Diagnostic showing where AI would pay and where it would not, and then build and operate the systems end to end — AI agents on WhatsApp, voice and web, custom AI software and agentic workflows, the digital platforms and integrations underneath them, and the search and campaigns that feed them.

Why do you not publish prices?

Because we do not know what your business needs until we have looked at how it runs. A published price list forces a lowest-common-denominator quote and invites you to compare a system designed around your process with an off-the-shelf bot. Scope comes out of your free plan, and the price is then fixed in writing before anything is built.

Is the AI Opportunity Diagnostic really free?

Yes, and it is yours to keep whatever you decide — including if you take it to another firm or build it with your own team. It costs you one 30-minute call, and the written plan lands within 24 hours. We do it free because we would rather earn the work by being useful first.

Do you only advise, or do you build it as well?

We build it, and then we run it. The people who study your business are the people who write the code, so nothing is lost handing over to a delivery team you have never met. After launch we keep monitoring accuracy, catch eval regressions, migrate models when providers deprecate versions, and hold running costs under a ceiling you approved.

What can you actually build?

AI agents on WhatsApp, voice and web; custom LLM applications and RAG systems over your own data; agentic workflows that call your systems; document and data extraction into your ERP or CRM; AI features inside your own product; and the websites, web applications, integrations and data pipelines underneath them. Plus the search, content and campaigns that generate demand for what we build.

How quickly can something be live?

Faster than the market expects, because we build with agentic tooling rather than by hand. A lead-qualification system goes live in three to five days, a WhatsApp agent in five to seven, a voice or support agent in seven to ten. Custom AI software runs four to six weeks depending on the integration surface. The free diagnostic itself is a 30-minute call and a written plan within 24 hours.

Start by finding out what is worth building.

Thirty minutes of your time, and a comprehensive written plan within 24 hours — costed against your own numbers and sequenced. Yours to keep.