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Written scope within 48 hours

AI that ships into the workflow, not a slide deck.

We design and ship LLM apps, copilots and automation for teams who need AI inside real processes, whether that runs through the backend behind a mobile or web app or a standalone pipeline. Evaluation and guardrails are part of the build, and how we handle IP, NDAs and security is settled before any of your data moves. It is the same discipline behind software we operate in production today.

Nobody budgets for the hours already being lost. Tell us the outcome: you get a written scope within 48 hours.

  • 48h

    To a written scope

  • Week 1

    Clickable prototype

  • 6–14 wks

    To first release

  • 100%

    Code and IP yours

Get your free estimate

Three quick questions: scope, approach and a price range back within 48 hours. No sales call required first.

What do you need built?
When do you want to start?
Where should we send the estimate?

Answer all three questions above, then send.

NDA-friendly · IP yours from day one

Trusted by companies worldwide

  • Bloomford
  • Contractor Plus, Inc.
  • Snappy Stats
  • Ad Lunam
  • Local Buzz
  • CyberFind
  • Hellory
  • 5.0Clutch · 8 client reviews
  • 4.9Google · 18 reviews
  • Top ratedUpwork · 100+ reviews · 5K+ hours worked
  • TopTracker7K+ hours tracked with clients

Why teams call us

You are probably here for one of three reasons.

Every AI Development project we take on starts from one of these. Recognise yours and the rest of this page gets more useful.

  • Support and triage eat a full-time job

    Every ticket, application or lead gets read and routed by a person before anything happens to it. That is the queue an LLM workflow removes first.

    Automate the routing, keep a person on the exceptions.

  • The chatbot everyone tried once

    A generic assistant that does not know your product, your prices or your policies, so nobody trusts what it says.

    Ground every answer in your own data, with citations.

  • A pilot that never left the demo

    Someone wired up an API call and showed it in a meeting. No evaluation, no guardrails, no plan for what happens when the model is wrong.

    Add the evaluation harness and guardrails a demo skips.

A research desk with a results chart, a pipeline diagram and annotated documents
Data · pipeline · results

What you actually get

Six things you can hold, not a slide deck.

Anyone can demo a prompt. These are the artefacts that land in your hands, and roughly when.

  1. A written scope, before anything is built

    Which use case ships first, which model or provider it runs on, and the per-call cost band before you commit budget.

  2. A clickable prototype in week one or two

    Real prompts and real outputs from your own data, in front of users before a token of it goes to production.

  3. Working software every two weeks

    A demo plus an evaluation report: accuracy on a fixed test set, latency and cost per call, not just working software.

  4. Code in your repositories from day one

    Your GitHub, your cloud accounts, prompts and evaluation sets under NDA, with commit access from the first sprint.

  5. Tests on the paths that matter

    Automated coverage plus guardrails against hallucination and prompt injection on the flows where a wrong answer costs you money.

  6. Documentation and a handover

    Model choice, prompts, evaluation sets and architecture notes, so your team can retune it without us.

What it costs

Three ways to start, priced honestly.

Ranges, not single numbers, because the real figure depends on integrations and migration scope. You get the exact number in writing after one call.

  • Launch

    One core journey, production-ready.

    Investment

    From mid four figures USD

    Timeline

    6–8 weeks

    • Discovery and written scope
    • Clickable prototype before build
    • Core journey in production
    • Auth, roles and admin basics
    • Your repos and cloud accounts
    • 30 days post-launch support

    For founders validating a product with real users.

    Start with Launch

  • Most chosen

    Scale

    A full product with the integrations that matter.

    Investment

    From low five figures USD

    Timeline

    10–14 weeks

    • Everything in Launch
    • Two to four external integrations
    • Payments, billing or subscriptions
    • Reporting and analytics
    • Automated tests on critical paths
    • Fortnightly demos and reports

    For teams putting one AI workflow in front of real users.

    Start with Scale

  • Platform

    A standing team that keeps shipping.

    Investment

    Retainer or dedicated pod

    Timeline

    Ongoing

    • Everything in Scale
    • Named technical lead
    • Multi-tenant or multi-entity architecture
    • Performance and cost optimisation
    • Monitoring, uptime targets, on-call
    • Roadmap planned quarter by quarter

    For products where the roadmap does not end at launch.

    Start with Platform

Ranges reflect projects delivered in 2025–26 and move with integrations, data migration and compliance scope. Every engagement starts with a written scope listing exactly what is included and what is not.

Our clients · reviews

Teams who trusted QalbIT

Short feedback from clients and products we work on across web, mobile, SaaS and internal tools.

  • Testimonials from long-term custom software development and SaaS product clients.
  • Covers ERP systems, B2B marketplaces, booking platforms, and web & mobile app development projects.
  • Real text and video reviews from founders, CTOs, and product teams who partnered with QalbIT for delivery.

  • Video review

    Rated 5 out of 5.
    “Working with QalbIT was an absolute pleasure. They understood our brand vision, delivered a WordPress site that is easy to navigate, and kept communication smooth throughout. I would absolutely recommend them to anybody.”
    Imdad Ali KadiwalaFounder, Netzur

  • Written review

    Rated 5 out of 5.
    “QalbIT is a fantastic dev partner. They provide quality work, good communication and go above and beyond. Fantastic work delivered by QalbIT and his team. Can’t give enough credits with regards to how they work and at which speed they do it. Top team.”
    Joost HesselberthFounder of Ad Lunam Investments PTY LTD

  • Written review

    Rated 5 out of 5.
    “I would recommend QalbIT to everybody. QalbIT has been developing all my front-end & backend systems complete to my requirements. They have excellent technical knowledge and listen very well to the needs of their customers. And above all, they are great people.”
    Wouter SantensExecutive Search Consultant at Bloomford

  • Written review

    Rated 5 out of 5.
    “They listened to my ideas and came up with a plan for development then executed that plan to completion. I liked their attention to detail and communication skills. Very responsive.”
    Osmond MwanyikyCTO, Bocsit

  • Written review

    Rated 5 out of 5.
    “QalbIT quickly integrated four platforms quickly. Square, Stripe, PayPal, and Coinbase were all successfully used. The vendor provided an active communication process, maintaining timely deadlines despite the research-heavy service. They also offered post-launch support.”
    Roshan SethiaCTO, Contractor Plus, Inc.
View all testimonials

How it runs

From first call to running software in four moves.

The same process on every AI engagement, whichever package you start from.

  1. Scoping call

    A working call about the use case, the data it touches and the constraints. Not a pitch.

    Day 1–3

  2. Written scope & price

    Architecture options, timeline and a price range you can take to your board.

    Within 48 hours

  3. Prototype & build

    Clickable prototype grounded in a sample of your real data, then working software in fortnightly sprints.

    Week 1 onward

  4. Launch & operate

    Deployment to your cloud, evaluation and cost monitoring in place, then the next slice.

    Launch week

Proof

Three ai development engagements we scoped, built and still support.

Different sectors, same process, and in each case the client owns the code.

How we work

A product engineering culture built on ownership and clarity.

The habits that decide whether software still works in year three. Here is the honest comparison: what most outsourced engagements look like, and what changes when you work with us.

How an engagement with QalbIT compares
The questionThe usual answerAt QalbIT
Who scopes itA salesperson, then a handover docThe founder and the engineer who will build it
Who writes the codeWhoever is on the bench that monthNamed senior engineers, in our office, no subcontractors
What you see weeklyA status deck and a percentageA working demo and the board it came from
When something breaksA ticket queue and an account managerThe engineer who wrote it, in your timezone overlap
If you want to leaveA migration project and a licence conversationThirty days notice · repos and IP were always yours

  • Principle 01

    Ownership over ticket-taking

    Engineers who ask why before they ask which framework. Scope questions come back before code goes out.

  • Principle 02

    Written over remembered

    Scope, estimates and architecture decisions are written down with the trade-off stated. Nobody relies on what was said on a call.

  • Principle 03

    Small over staffed

    Senior pods that hold the whole system in their heads beat large mixed teams that need a coordinator to function.

Engagement models

Flexible engagement models.

Choose how you want to work with us based on where your product is today. We help you validate, ship and then scale without forcing you into a one-size-fits-all model, and how we price projects follows the model you pick.

  • Best for defined scope

    Fixed-scope projects

    Best when requirements are clear and we can estimate precisely.

    • Clearly defined scope, milestones and deliverables.
    • Predictable budget with upfront estimates.
    • Ideal for MVPs and discrete feature releases.

    Works well with good documentation or a short discovery

  • Most chosen

    Dedicated product squad

    A cross-functional team that behaves like your in-house product team.

    • Stable team for long-term ownership and roadmaps.
    • Capacity planned monthly with clear velocity.
    • Founder involvement on architecture and key decisions.

    When software is core to your business

  • Best for ongoing work

    Time & material / Agile

    Ideal for ongoing iterations, R&D and integrations.

    • Pay for actual engineering and design time used.
    • Flexible backlog and scope, prioritised every sprint.
    • Perfect for experiments and proof-of-concepts.

    When agility matters more than fixed scope

What protects you

Five commitments that make the first engagement low risk.

The objections worth raising before you sign anything, answered up front rather than buried in a contract.

  • Free written scope

    Model choice, evaluation plan, timeline and price range within 48 hours, yours to keep whether or not you hire us.

  • IP and repos yours, day one

    Code, prompts, evaluation sets and cloud accounts in your name under NDA, with commit access from week one.

  • Changes quoted in writing

    A new use case or a swapped model is priced and approved before it is built. The number never moves quietly.

  • Replacement guarantee

    If an engineer is not the right fit we replace them at no recruitment cost, with handover before they leave.

  • 30 days notice

    Monthly engagements end or scale down with 30 days notice. No annual lock-in.

Industries we build this for

Tailored software solutions built for your industry's success.

We bring patterns from multiple domains, but always adapt them to your specific context, constraints and users, never a template dropped on your business.

More services

Software development services at a glance.

From MVPs to long-term platforms, combine our core and specialised services to design, build and grow products that match how your business really works.

FAQs · AI Development

Frequently asked questions

Cost, timelines, data handling and what happens once it is running in production.

Ask us directly →
Cost depends mainly on how much of the workflow the AI has to touch and how much evaluation and guardrail work a use case needs, not on which model you pick. Launch starts from mid four figures USD for one AI-assisted journey in production, and Scale starts from low five figures USD once it is wired into two to four of your existing systems. The written scope after one call gives the real number.
A clickable prototype, with real prompts against real data, typically lands in week one or two. A production release usually takes 6 to 8 weeks for one workflow, or 10 to 14 weeks once it is integrated with the systems around it and evaluation is in place.
We pick the model per use case rather than defaulting to one vendor, and the written scope names it along with why: cost, latency, data handling terms, or a specific capability the task needs. If you have a preference or a procurement restriction on a particular provider, that is a constraint we scope around, not a fight.
Evaluation and guardrails are part of the build, not a pass added after launch: grounding answers in your own data rather than the model's general knowledge where accuracy matters, testing outputs against known-good answers before release, and adding a human check on anything the workflow cannot afford to get wrong.
How your data is handled, whether it is used for model training, where it is stored, and who can see it, is settled in writing before anything moves, following the same security and NDA terms as any other engagement. Where a provider's default terms do not meet what you need, we scope a configuration or a different provider instead.
Most of our AI work plugs into an existing product rather than replacing it: through the backend behind a web or mobile app you already run, or as a workflow that reads from and writes to systems you already use. A rebuild is only proposed when the current architecture genuinely cannot support it, and we say so plainly rather than defaulting to a bigger project.
The same discipline as any other feature that could fail expensively: automated tests on the paths where a wrong answer costs money or trust, a set of known cases checked before every release, and monitoring in production so a drop in quality is caught rather than reported by a customer first.
Model usage is billed per call by the provider, and that cost scales with how often the feature is used, not with headcount. We size that ongoing cost as part of the written scope so it is not a surprise on the first invoice after launch, and design the workflow to avoid calling the model where a cheaper check will do.
Yes, before build starts, following the same terms as the rest of our engagements: what data moves, where it is processed, and under what agreement. Nothing touches a live system or real customer data without that being settled first.
Model behaviour and provider pricing both change over time, so most clients keep a small standing team to monitor accuracy and cost and to extend the workflow as the process around it changes. It is optional, with 30 days notice, and you keep the code, prompts and infrastructure either way.

Next step

Get the number before you commit.

Send the problem in two lines. You get a written scope, timeline and price range within 48 hours, free, and yours to keep either way.

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