We sit with your team, map the work, and automate the repetitive parts.

Your people stay in charge of what matters. Engineering, not slide decks, and you own everything we build.

Contact us See example deliverables

Most AI pilots stall before they reach daily work.

Gartner forecast that at least 3 in 10 generative AI projects would be abandoned after the proof of concept. In S&P Global's 2025 survey, more than 4 in 10 companies had dropped most of their AI initiatives.

The reasons are rarely the AI model. They are:

  1. The wrong problem.

    Haneex We observe first.

  2. Data that is not ready.

    Haneex We fix the foundations when they need it.

  3. A tool beside the workflow, not in it.

    Haneex We build into the steps people already take.

  4. No testing or controls.

    Haneex Nothing goes live without a signed-off evaluation.

Sources

How we work: listen, observe, plan, automate.

Every step ends with something you can read, keep and judge us by.

  1. Listen

    We interview the people who do the work and the people who own the outcome.

    You get: A one-page problem statement you agree with. See an example

  2. Observe

    Our engineers sit with your team and watch the real process, including the workarounds.

    You get: A work map: tasks, volumes, handoffs and where time goes. See an example

  3. Plan

    We rank what to automate by value, risk and effort, and say plainly what should stay human.

    You get: An automation plan and a fixed-scope proposal. See an example

  4. Automate

    We build in your environment, show working software every week, and test it before anyone relies on it.

    You get: A signed-off evaluation report, then a handover. See an example

What we do, by the result you want.

Automate the work

AI Strategy & Implementation

We sit with your team, map their daily work, and build AI into the steps that slow them down.

  • AI readiness audit and ranked use-case roadmap
  • Assistants on your own documents and data
  • Workflow automation with LLM agents and human review
  • Evaluation, guardrails and cost controls before launch
  • Team training so the work outlives the engagement
Typical first step
An Observe sprint on one workflow your team repeats every day
Tools
Major AI model providers chosen with you, Python, search over your own documents

Good fit when

"We know AI could help, but we don't know where to start, or our pilot never made it to production."

You walk away with

A ranked roadmap, then an AI feature your team uses every day, with tests that show it works.

Fix the foundations

Data Engineering & Snowflake

AI is only as good as the data under it. We build the pipelines and warehouse that make your numbers agree.

  • Snowflake setup, modeling and migration
  • Batch and streaming pipelines with tested transformations
  • Access roles, governance and cost monitoring
  • Dashboards and self-serve analytics on one source of truth
Typical first step
A data review: your sources, their quality, and which numbers disagree
Tools
Snowflake, SQL, Python, dbt

Good fit when

"Finance and operations have different numbers, and every report starts with an export to Excel."

You walk away with

One warehouse with clear access rules, tested pipelines feeding it, and dashboards people trust.

Cloud & DevOps

Infrastructure that deploys safely, recovers fast, and costs what you planned.

  • AWS and Azure architecture and migration
  • CI/CD pipelines, containers and infrastructure as code
  • Monitoring, alerting and tested rollback
  • Cloud cost reviews and right-sizing
Typical first step
A cost and reliability review of what you run today
Tools
AWS, Azure, Terraform, containers, CI/CD pipelines

Good fit when

"Deploys are scary, the bill keeps growing, and one server under someone's desk still runs payroll."

You walk away with

Automated deploys with one-click rollback, monitoring that pages the right person, and a bill you can predict.

Get an independent opinion

IT & Technology Consulting

Independent advice on what to build, buy or fix, before you spend the budget.

  • Technology and AI strategy, roadmaps and budgets
  • Architecture and security reviews
  • Vendor and platform selection
  • Process and systems assessment
Typical first step
One scoped question, answered in a written recommendation
Tools
Architecture diagrams, cost models, vendor scorecards

Good fit when

"We have five vendors pitching us and no one inside to tell us which one is right."

You walk away with

A clear, written recommendation with costs, risks and the order to do things in.

Build and run software

Technology Development

Custom software and AI products, designed, built and supported after launch by the same team.

  • Web and mobile applications
  • AI products, assistants and agents
  • Internal tools, portals and integrations
  • Our own platforms, built and run in-house
Typical first step
A scoped first release that real users can try
Tools
TypeScript, Python, React, cloud-native services

Good fit when

"Nothing off the shelf fits how we work, so we need something built for us."

You walk away with

Production software you own, with tests, documentation and a team that can support it.

Application Maintenance & Support

We keep your applications and AI automations healthy after launch, whether we built them or someone else did.

  • Monitoring and incident response
  • Bug fixes and updates
  • Security patches and dependency upgrades
  • AI model and prompt updates, with checks that answers stay accurate
  • Performance and cloud cost tuning
  • Small enhancements as your needs change
Typical first step
A health check of the application and how it is run today
Tools
Common web, cloud and database stacks; we say up front if yours is outside what we know

Good fit when

"Our app works, but nobody owns it anymore, and every change feels risky."

You walk away with

A team that watches your systems, fixes what breaks, and keeps them up to date.

Or start with a ready-made package

Well-defined starting points for problems we see often. Each one is scoped with you before work begins.

AI Readiness Assessment

Find and rank where AI pays back in your business.

  • Interviews with the people doing the work
  • Data and systems review
  • Ranked use-case roadmap with costs
Request this →

Knowledge Assistant

An assistant that answers from your own documents, with sources.

  • Connects to your files and wikis
  • Answers cite their source
  • Respects who may see what
Request this →

Document Automation

Invoices and forms read, checked and routed for you.

  • Extraction from PDFs and scans
  • Validation against your rules
  • People approve only the exceptions
Request this →

Data Platform Launch

One warehouse and one set of numbers everyone trusts.

  • Snowflake setup and modeling
  • Tested pipelines from your systems
  • Dashboards on the shared data
Request this →

Cloud Cost & Reliability Review

A cloud that deploys safely and costs what you planned.

  • Cost and right-sizing review
  • Monitoring and alerting check
  • Rollback and recovery tested
Request this →

What we commit to in writing.

These are in every contract, not just on this page.

  1. You own what we build

    Code, prompts, configurations and documentation belong to you. Open-source or pre-existing parts are listed up front.

  2. It runs in your cloud

    We deploy into your own AWS or Azure account. AI model calls go only to the provider you approve.

  3. The same engineers, start to finish

    Nobody is swapped in without your agreement.

  4. Tested, shown and handed over

    Nothing goes live until you sign off a written evaluation report. Handover includes documentation, runbooks and training.

Small steps, each one a clean place to stop.

No open-ended contracts. After every step, you decide whether to continue.

  1. Observe sprint

    Fixed fee

    We listen and observe, then hand you a work map, an automation plan and a fixed quote. Stop here if you like: the plan is yours.

  2. Pilot

    Fixed scope, paid by milestone

    One workflow automated in your environment, with an evaluation report that shows whether it is good enough.

  3. Production build

    Paid by milestone

    The full build with weekly demos, guardrails, monitoring and a proper handover.

  4. Support and run

    Monthly, cancel with notice

    We keep it healthy: monitoring, fixes, updates and re-running the evaluation when models or data change.

Just need advice? Architecture reviews and vendor selection are a fixed fee per question, with a written recommendation.

We are a good fit for

  • Finance and operations teams with repetitive, document-heavy or reconciliation-heavy work
  • IT leaders who want AI to run inside their own cloud, under their own access rules
  • Teams whose AI pilot worked in a demo but never made it into daily use

We are not the right choice for

  • Staff augmentation: we do not place contractors on your team
  • A strategy deck with nothing built: our advice comes with working software, or a clear reason not to build
  • AI with no human review: people approve anything that changes a record or leaves your business

See what you get before you hire us.

We are a new company, so instead of case studies, here is the actual shape of our work. These examples use a made-up company and made-up numbers.

  • Example deliverable

    Work map

    What we hand over after Observe: every step of an invoice process, with volumes, handoffs and time spent.

    Read the work map example
  • Example deliverable

    Automation plan

    What we hand over after Plan: ranked opportunities, what stays human, risks, and the proposed first build.

    Read the automation plan example
  • Example deliverable

    Evaluation and guardrail report

    What you sign before go-live: how the system was tested, what it got wrong, and the controls around it.

    Read the evaluation and guardrail report example

Straight answers.

Ownership and people

Who owns the code and everything else you build?

You do. Code, prompts, configurations and documentation are assigned to you in the contract. If we use open-source or pre-existing components, we list them before work starts.

Who will actually work with us?

Named engineers, written into the statement of work. The people who observe your process build the solution, and we do not swap them without your agreement.

Do you provide contractors or staff augmentation?

No. We deliver defined outcomes with our own engineers rather than placing people on your team.

Data and security

Where does our data go, and which AI providers see it?

We build inside your AWS or Azure account. If the system calls an AI model, it calls only the provider you approve, under your account and settings, and we document the data flow so your security team can review it.

Have you had an independent security audit?

Not yet. Haneex is a new company. We work inside your environment under your controls, with least-privilege access, audit logging and encryption, and we will answer your security questionnaire in writing.

Which clouds and tools do you work with?

AWS and Azure; Snowflake for data; Microsoft 365, common ERPs and document stores as sources; and the major AI model providers, chosen with you.

Price and proof

How does pricing work?

The Observe sprint is a fixed fee. After it you get a fixed quote for the next step. Pilots and builds are paid by milestone; support is monthly. If the scope needs to change, we agree it in writing before any extra work.

What if the pilot shows automation is not worth it?

Then we tell you, with the evidence. You keep the work map and the plan, and you can stop there. Every step is a clean exit point.

Do you have case studies?

Not yet, and we will not invent them. Instead you can read our example deliverables, and you will meet the engineers who will do your work before you sign.

Do you resell software or take fees from vendors?

No. We do not take resale margins or referral fees from the vendors we recommend, so our advice is independent.

Quality and control

How do you know the AI is accurate enough?

Before launch we test it on real examples from your work, measure how often it is right, record where it fails, and agree the bar with you. You sign off the written evaluation report before anything goes live.

Do people stay in control of automated work?

Yes. Automations draft, check and route work, but a person approves anything that changes a record, moves money or leaves your business.

Bring one process you would like to stop doing by hand.

We will tell you honestly whether it is worth automating, including when it is not.