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Smarter systems. Real outcomes. Built for what's next. The heurixNext delivery path rises from Automate to Integrate to Optimise to Deliver over a city skyline at sunrise.

AI and automation, built to work.

heurixNext designs and delivers software systems that automate what slows your business down — from first architecture decision to production deployment.

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  • Financial services
  • Logistics
  • Hospitality
  • Professional services
  • Healthcare technology

The problems we solve

You're running manual processes that should be automatic

Your team is spending hours on work software should handle. The roadmap to fix it exists; the capacity to build it doesn't.

You've evaluated AI tools. None of them fit.

Off-the-shelf AI products solve generic problems. Your problem is specific. A custom solution built on the right foundations beats any SaaS subscription.

You need to move fast without creating technical debt

A quick fix today can cost ten times as much to undo in eighteen months. The right architecture on day one is cheaper than refactoring on day three hundred.

If any of these sound familiar, we should talk.

What we do

AI Engineering

From proof of concept to production pipeline.

Process Automation

Every hour your team spends on repetitive work is an hour you're paying for twice.

Enterprise Architecture & Transformation

The right foundations last. The wrong ones cost you.

Technical Delivery & Advisory

Engineering leadership, on demand.

SEO & GEO

Ranking in search isn't the whole game anymore — being cited by AI is.

The engagement

How an engagement works

Nine steps run from the first call to the day your team takes it over. You see three phases — the rest is work that happens inside them.

Discovery

We understand the problem before writing a line of code.

Absorbs steps 01–04: initial call, problem mapping, feasibility and scope.

Build

We deliver in short cycles with visible progress at every step.

Absorbs steps 05–07: sprint zero, iterative build, integration and QA.

Handover

You own the output. We ensure your team can run it.

Absorbs steps 08–09: staged deployment, documentation and training.

Six of the nine steps are absorbed into the three phases above. The work still happens — it stops being something you have to run.

Engagements typically run 6–16 weeks. We don't do retainers that create dependency. We build things you can run without us.

What this looks like in practice

Illustrative examples of the problems we take on and how we approach them. Client case studies are available on request.

Hospitality

Automating a multi-property operational workflow

Groups running several properties often have each site repeating the same manual task — rotas, stock counts, daily reporting — by hand and in its own way. We map the process across sites, find the steps that don't need a person, and build one automated workflow every property runs the same way.

Staff time moves from re-keying data to running the property.

Financial services

Retrieval-augmented document classification

Firms receiving large volumes of contracts, statements or correspondence often sort and tag them by hand. We build an AI pipeline that reads each document, classifies it, extracts the key fields and routes anything uncertain to a person for review.

Reviewers handle the exceptions, not the whole queue.

Logistics

Taking the manual effort out of supplier onboarding

Supplier onboarding often lives in email threads and spreadsheets, with documents chased by hand and no clear view of where each supplier is stuck. We build a workflow layer that collects documents, runs checks and shows every supplier's status in one place.

Onboarding becomes a tracked process rather than an inbox.

Recently built

Products we have designed and built end to end — client proofs of concept and our own tools. Clients are described rather than named.

Professional services · Client projectProof of concept

ClientPulse Automated fee and deadline reminders for an accountancy firm

A London accountancy firm was chasing late payments and statutory filing deadlines by hand. ClientPulse imports the firm's client spreadsheet, works out who needs a reminder and when, and sends SMS inside UK business hours — skipping bank holidays and anyone who has opted out.

  • Safe by default: dry-run mode, kill switch, monthly spend cap and a send allowlist
  • Guided spreadsheet import with column mapping and row-level validation
  • Audit trail on every change, with role-based sign-in
  • .NET 9
  • ASP.NET Core
  • EF Core
  • xUnit
Education · Client projectWorking MVP

HeurixApp Digital hours claims for school support staff

Support staff at a UK school claimed extra hours on paper, with totals worked out by hand and missing claims spotted only at the payroll deadline. Staff now log shifts from their phone; administrators see every claim in one table, lock the month and export payroll-ready files.

  • Hours calculated on the server, so there is no hand-rounding drift
  • PDF export matching the local authority's existing form, plus CSV for payroll
  • Multi-tenant from day one — each school's data isolated by row-level security
  • Next.js
  • TypeScript
  • Supabase
  • Postgres
Marketing · Internal toolIn dry-run

Nexa An AI content assistant with a human in the loop

Nexa turns rough notes into LinkedIn drafts in the founder's own voice, plans the week ahead and learns from what performed. It is built on agent orchestration rather than a bespoke app — and nothing publishes without explicit approval.

  • Approval is a signed token tied to the exact text; any edit voids it
  • Compliance check blocks unsourced statistics before a draft reaches review
  • Approve, edit or reject from Telegram, with every action logged
  • Claude
  • Agent workflows
  • Python
  • Buffer API
Consumer · PrototypePrototype

CalorieTracker Nutrition logging from a photo or a sentence

Type “chicken wrap and a latte” or upload a photo of a meal, and a multimodal AI model breaks it into items with calories and macros, ready to log in one click. No account needed — the food log is stored locally.

  • Meal recognition from photos using a multimodal model
  • Structured model output, validated before it reaches the log
  • AI lookups cached into a local food library
  • Next.js
  • TypeScript
  • Gemini

Why heurixNext

How heurixNext compares with a large consultancy, a freelancer and an in-house hire
CriterionLarge consultancyFreelancerIn-house hireheurixNext
Speed to start6–12 weeks1–2 weeks3–6 months1–2 weeks
Architecture depthHighVariableVariableHigh
Commercial flexibilityLowHighLowHigh
AccountabilityDiffuseIndividualInternalCompany-level
Scale pathYesNoPartialYes — built in

We are a company, not a contractor. Every engagement is backed by heurixNext as an organisation — SLAs, insurance, a senior team, and a single point of accountability.

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Based in the UK. Working with clients globally.