SmartLink
AI & automation

AI aimed at a specific job.

Choosing an AI development company in Pakistan is largely a question of whether the supplier will tell you when the answer is no. SmartLink Services runs this practice from Karachi and the work is narrow by design: reading supplier invoices into an ERP, forecasting demand for a product family, answering questions from a document set people currently search by hand. What follows is the part buyers ask about before they commit any budget. What the work costs at market rates, how long a first measurable result takes, whether you need a custom model at all, how your data is handled, and what it costs to keep something running once it is live.

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Start
One measurable process
Grounding
Your documents and data
Control
Cost, access and audit
Exceptions
Routed to a human
Overview

The useful AI projects are narrow, measurable and boring.

AI work goes wrong in a predictable way. It starts as a broad ambition, produces an impressive demonstration, and then stalls because nobody defined which decision it was supposed to improve or how anyone would know if it had.

We start from the opposite end: one process, a current cost in hours or errors, and a target that can be measured afterwards. Reading supplier invoices and posting them. Forecasting demand for a product family. Answering questions from a document set that people currently search by hand.

Two rules apply throughout. Answers are grounded in your own documents and data with citations, so responses can be checked rather than trusted. And exceptions go to a person by design, because the value is in handling the routine cases cleanly, not in pretending the edge cases do not exist.

Scope

What AI solutions covers

Engagements usually begin with one process, measured, before anything is widened.

What the work covers

  • Identifying a process where AI has a measurable effect
  • Document extraction: invoices, purchase orders, delivery notes and forms
  • Validation against purchase orders, contracts or master data
  • Forecasting models built on your own transaction history
  • Assistants grounded in your documents, answering with citations
  • Cost, access and audit controls before anything goes live

What you get at handover

  • A working automation on one real process
  • Accuracy measurement against a human baseline
  • Exception handling routed to named owners
  • Cost and usage monitoring
  • Documentation covering limits and failure modes

Typically involves

Document AI Vector search ERP integration
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Services in this practice

Every AI solutions service

AI & automation

AI document processing

Invoices, purchase orders, delivery notes and forms read automatically into your systems.

Open service page → AI & automation

Forecasting & analytics models

Demand, inventory and cash forecasting models built on your transaction history.

Open service page → AI & automation

AI assistants over enterprise data

Question-answering assistants grounded in your documents and ERP data, with citations.

Open service page → AI & automation

Process automation

Rule-based and AI-assisted automation of the approvals and reconciliations that eat time.

Open service page → AI & automation

AI integration & governance

AI capability connected into existing applications, with cost, access and audit controls.

Open service page →
01

Judging whether a process is a candidate before spending anything

Not every manual process is worth automating, and the ones that look most tedious are not always the best candidates. Four things make a process suitable: enough volume for a saving to be material, enough consistency for the pattern to be learned, an outcome that can be checked against something, and a tolerable cost when it goes wrong. A high volume process with no way of telling whether an answer was right is not a candidate, it is a liability waiting for an audit.

One test settles most arguments quickly. Give the source documents to a capable person who knows the business but has not seen these particular files, and ask them to produce the answer. If they cannot, because the information is not actually present or because it depends on knowledge that lives in somebody's head, no model will do better. That costs an afternoon and it has saved more than one organisation from funding a project that could not have succeeded.

History is the other prerequisite, particularly for forecasting. A model built on transaction history inherits everything about that history, including the year the plant closed for six weeks and the period when a discount code was miscoded. Understanding those distortions is a real part of the work. Where history is too short, too clean, or the product is genuinely new, a stated statistical baseline and a person's judgement will beat a model, and we will tell you which situation you are in.

  • Volume, consistency, verifiability and cost of error assessed before any build is quoted
  • A capable person given the same inputs as a control, before a model is considered
  • Transaction history reviewed for known distortions before a forecast is attempted
  • The owner of the current process involved from the first conversation
  • An explicit decision to stop where the process is not a suitable candidate
02

Building the evaluation set before building anything else

The first artefact of a serious project is not a prototype, it is a set of examples with agreed correct answers. A few hundred real documents, or a held back period of history, with the right result recorded by the people who do the work today. Assembling this is tedious, and it is the single most valuable day anyone spends on the project, because without it no claim about accuracy means anything and every disagreement becomes a matter of impression.

Assembling that set surfaces something uncomfortable and useful. Two experienced people will often disagree about the correct answer, on documents they both handle every week. The disagreement is not noise, it is an undocumented rule, and resolving it improves the manual process regardless of what happens with any software afterwards. Where a rule genuinely cannot be settled, that category is marked as one for a person to decide and excluded from the automation entirely. Recording the resolution matters as much as reaching it, because the same question returns with the next reviewer.

Measurement continues after launch, and this is the part most often dropped. Model providers update their systems, documents change format when a supplier redesigns an invoice, and a process that was accurate in March degrades quietly by September. Re-running the evaluation set on a schedule turns that into a visible number rather than a slow accumulation of corrections. Any change to a prompt, a model version or a confidence threshold is treated as a release and measured again before it reaches production.

  • An agreed evaluation set built from real cases before development begins
  • Disagreements between experienced reviewers resolved and recorded as rules
  • Categories that cannot be settled excluded from automation by design
  • The evaluation set re-run on a schedule and after any model or prompt change
  • A defined accuracy threshold below which the automation is paused
01

What an AI development company in Pakistan charges

Nobody publishes an honest price list for this work, and the reason is worth stating rather than hiding. Reading one supplier's invoice layout into an ERP and forecasting demand across a twelve year transaction history are not the same job, do not need the same people, and cannot be quoted from the same table. Any firm advertising a fixed price for artificial intelligence is selling a demonstration. What you can compare are rates and the shape of the engagement.

Rate cards in Pakistan are public enough to be useful. Junior developers are quoted at PKR 500 to 1,000 an hour, a full stack developer at PKR 3,500 to 8,000, and a senior specialist at the top of that band. Billed internationally the same work is quoted at USD 15 to 50 an hour, with premium agencies at USD 40 to 80. Pakistani rates sit roughly 60 to 80 percent below Western equivalents. The closest published project comparator is custom web application work, at PKR 200,000 to 1,000,000 and above, which is a fair reference point because an automation is delivered as custom software wrapped around a model rather than as a model on its own.

Four things move that number. Document variety is the largest, since every supplier who redesigns an invoice is a separate case to handle. Whether anybody has ever recorded the correct answers comes second, because assembling that set is real effort with no shortcut. Integration is third: posting into an ERP with validation against a purchase order is more work than producing a spreadsheet. Then the exception route, since somebody needs a screen that shows what the system was unsure about.

Two costs sit outside the build price and are the ones clients meet later. Model consumption is metered by the provider and rises quietly as more teams find the tool useful. Exception review is staffed by people, and they need more expertise than the team that previously processed everything. We scope and price a pilot on one process first, and quote the build only once that pilot has produced a measured number.

  • Rate cards comparable across suppliers, fixed prices for artificial intelligence are not
  • Document variety and the absence of recorded correct answers drive most of the effort
  • Integration and exception handling priced as software, because that is what they are
  • Metered model consumption and reviewer time budgeted separately from the build
  • A pilot on one process scoped and priced before any wider build is quoted
02

How long an AI project takes

Publicly reported delivery timelines in this market fall into two bands, and the same shape holds here. Six to twelve weeks covers a focused single scope go live, which for us means a pilot on one process, measured against a human baseline and running on real documents. Three to six months is the honest band once several processes, a forecasting model or integration across systems are involved. An AI development company in Pakistan can build a demonstration in a fortnight, and it proves almost nothing.

Unlabelled data is the commonest reason a project takes longer than the plan said. The documents exist, the history exists, and nowhere has anybody written down what the right answer was. Assembling a few hundred real cases with agreed correct answers takes days of an experienced person's time, and surfaces disagreements between reviewers who handle these documents every week. That is useful, and it is not fast.

Access is the quieter delay. Extracting two years of transactions from a production database needs a named owner, a window and often a legal view on what may leave. Where a forecast is involved, the history then has to be understood before it is modelled, including the six weeks the plant was closed and the quarter when a discount code was miscoded.

We say so plainly when history is too short, too clean or the product is new, because a stated baseline and a person's judgement will then beat a model and cost far less to run.

  • Six to twelve weeks reported for a focused single scope go live in this market
  • Three to six months once forecasting or cross system integration are in scope
  • Recording correct answers on real cases treated as project work with a named owner
  • Database extracts scheduled with an owner, a window and a view on what may leave
  • An explicit recommendation to stop where the history cannot support a model
03

An off the shelf model, a custom model, or better data

A hosted commercial model behind an API covers most enterprise work we are asked for. Extraction, classification, summarising and grounded question answering are all served well by the current generation of general models, and the practical advantages are large: no training run, no graphics hardware to buy, and capability that improves without a project. The costs are equally real. You depend on a provider's roadmap and version changes, consumption is metered and grows with adoption, and your content crosses a boundary under contract terms somebody has to read properly.

Training or fine tuning your own model earns its cost in narrower circumstances than vendors suggest. It makes sense when a task is repetitive at genuinely high volume, when the vocabulary is specific to your industry in a way a general model handles poorly, when latency or data residency rules out a hosted call, or when the arithmetic of per unit cost at your volume beats the API. Recognise that the cost profile inverts. Large up front, small per use, plus labelled data and somebody who will retrain it when the world moves.

Fixing the data instead is the option that gets least attention and pays back most often. Duplicate suppliers, customers spelled three ways, materials created with whatever fields the requester felt like completing: a model built on that inherits every one of those problems and adds a layer that makes them harder to see. Cleaning the master data and writing creation rules is unglamorous work that improves every report you already run, whether or not a model ever follows.

An AI development company in Pakistan that only ever recommends a model is not giving you advice. Sometimes the honest answer is a deterministic rule, a lookup table or a corrected form, and it is cheaper, faster and auditable. AI is the wrong tool where volume is low, where there is no way to check whether an answer was correct, or where an error is expensive and nobody can fund the review that would catch it. We will say which situation you are in, and quote for the rules based version when that is the right one.

  • A hosted commercial model suits most extraction, classification and question answering work
  • Fine tuning justified by volume, specialised vocabulary, latency or residency, not by preference
  • Master data clean up often delivers more than a model, and improves existing reporting
  • Deterministic rules preferred wherever they can produce the same outcome
  • A stated recommendation against AI where verification or review cannot be funded
04

Data handling, oversight and the rules that apply

Where your data goes is the decision to take slowly and write down. Which documents leave your environment, which provider processes them, in which region, whether anything is retained and for how long, and whether the terms exclude training on your content. Ask any AI development company in Pakistan for those answers in writing. They are contractual, and we obtain them before a prototype touches a real record. Logs and monitoring belong in the same conversation, because request content ends up in both and is routinely forgotten.

Pakistan is a specific case. There is still no enacted comprehensive data protection statute here. A Personal Data Protection Bill has gone through several drafts and consultation rounds without passing, so obligations for a Karachi business come from its contracts, its sector regulator and its customers rather than a national law you can point at. That makes the written agreement between you, us and any provider the actual control, so we treat it as a deliverable.

Organisations operating in Europe have a further consideration. The EU AI Act imposes duties that scale with the use rather than the technology, and employment screening or creditworthiness is treated far more strictly than reading a supplier invoice. Documenting intended purpose, known limits and human oversight is sensible everywhere and required in some places. Whether a use is classified as high risk is a legal determination for your advisers. We produce the technical evidence they ask for and do not offer that opinion.

Every model will eventually produce a confident answer that is wrong, and the design assumes it. Answers are grounded in your own documents with citations so a person can check them. Extracted values are validated against purchase orders and master data. Confidence thresholds send uncertain cases to a reviewer instead of posting silently. A human stays in the loop wherever an action moves money, changes a master record or goes to a customer, and the evaluation set is re-run after any change of model version, prompt or threshold.

  • Processing region, retention and training exclusions agreed in writing before a prototype
  • Logs and monitoring included in residency decisions rather than treated as infrastructure
  • Pakistan has no enacted data protection statute, so contracts and sector rules govern
  • EU AI Act obligations assessed by your legal advisers, with technical evidence from us
  • A person in the loop wherever money moves or a master record changes
05

Cloud, on premise and the cost of running a model

Hosted inference is the default for good reasons. There is no hardware to buy, the provider patches and upgrades the model, capacity absorbs a busy month without planning, and you pay for what you use. Against that, content leaves your network under contract terms, the bill scales with adoption rather than staying flat, and a provider's version change can alter behaviour you had measured. That last point is why we re-run the evaluation set when a version moves.

Running open weight models on your own machines flips every one of those trade offs. Nothing crosses the boundary, the cost is capital you have already committed whether the hardware is busy or idle, and the obligation is yours: graphics capacity, drivers, model updates and a person who is competent to do them. It is a sound choice where residency is a hard requirement from a regulator or a parent company, or where volume is high enough and steady enough that metered pricing stops making sense.

A regional deployment or a private endpoint from a commercial provider sits between the two and satisfies most residency concerns without the hardware. Whichever route is chosen, budget for the running cost rather than the build alone: metered inference, storage for whatever index the assistant searches, reviewer time on the exception queue, and re-evaluation after each change. Budgets, alerts and per process cost attribution go in before the first live run, not after the first surprising invoice.

  • Hosted inference chosen for elasticity, patching and no capital outlay
  • Own hardware chosen for residency requirements or high steady volume
  • Regional deployments and private endpoints offered as a middle route
  • Evaluation set re-run whenever a provider changes a model version
  • Inference, storage, reviewer time and re-evaluation budgeted as running cost
How we deliver

Delivering AI solutions

One process, a cost in hours today, and a target that can be measured afterwards. These six steps add something the other practices do not need: an evaluation set built before anybody writes a prompt.

  1. 01

    Discover

    Volume, consistency, how an answer would be checked, and what a wrong one costs. We also give a capable person the same documents as a control, because if they cannot produce the answer, no model will.

  2. 02

    Blueprint

    A few hundred real cases with agreed correct answers, assembled with the people who do the work now. Where two experienced reviewers disagree, that is an undocumented rule, and it gets settled here.

  3. 03

    Build

    Extraction, validation against the purchase order or master record, confidence thresholds and the exception queue are built together. Retrieval is filtered by the asking user's existing permissions, never after the answer is generated.

  4. 04

    Test

    The evaluation set is run and scored against the human baseline, per document type, so you can see where accuracy holds and where it does not. Thresholds are set from that, not from a default.

  5. 05

    Go live

    Live on one process, with cost budgets and alerts already configured, exception owners named, and a written route back to the manual process that the people who would use it have rehearsed.

  6. 06

    Run

    Re-run the evaluation set on a schedule and after any model or prompt change, because a supplier redesigning an invoice can degrade accuracy quietly. Below the agreed threshold, the automation pauses.

Working together

Narrow, measured and reversible

Three words describe everything above. Narrow, because a defined process can be judged and a broad ambition cannot. Measured, because accuracy claimed is worthless while accuracy demonstrated against a real evaluation set is the whole argument. Reversible, because any system people cannot switch off will eventually be trusted rather more than it deserves.

Projects designed this way are less exciting to describe and considerably more likely to still be running a year later. They also compound. One measured automation gives an organisation the evaluation habit, the governance paperwork and the confidence to pick a second candidate on evidence rather than on enthusiasm.

SmartLink Services builds these systems, connects them to the applications you already run, and hands over the documentation and controls that make them defensible. Where the honest assessment is that a rules based automation or a corrected process will do the job with no model at all, that is what we will recommend and that is what we will quote for.

Credentials

Accreditations behind AI solutions

AI work reads from systems that already exist, so most of what follows is about the data platform underneath and the terms under which data moves.

Oracle

Partner

The data an assistant or forecast reads usually sits in Oracle. We build the extract views, document the schema, and use AI Vector Search in 23ai where retrieval belongs next to the data.

Microsoft

Partner

Azure hosts the processing where residency requires it, Power BI carries forecast output to the people who act on it, and SQL Server is often the source the model reads history from.

Google

Partner

Document AI handles the first pass on scanned and photographed invoices where layout varies, and Google Cloud gives a stated processing region when a document set cannot leave a jurisdiction.

FBR digital invoicing

Compliant integrations

Supplier invoices read automatically still have to reconcile to what was reported. Extracted sales tax values are checked against the purchase order and the return, with mismatches routed to a person.

HIPAA

Compliant practices

Clinical and claims documents are processed under a business associate agreement, with identifiers redacted where the task does not need them and provider terms that exclude training on your content.

Client words

What AI solutions clients say

Comments from people who run ai solutions systems day to day.

  • The handover was the part I judged them on. Configuration decisions documented with the reasoning, our administrators trained properly, and a checklist we actually worked through. We run it ourselves now, and calling them is a choice rather than a necessity.
    Head of Shared Services Multi site manufacturing group
  • What sold us was that they argued with our brief. We asked for a reporting layer and they came back saying the reporting was fine, the batch data underneath it was not, and fixing that first would cost less. That turned out to be right. Our first mock recall after go live took an afternoon instead of the better part of a week.
    Finance Director Food manufacturing group, Karachi
  • We had been through one failed implementation already, so we were sceptical of the whole category. The difference here was the migration work. Two full rehearsal loads before the real one, with a reconciliation pack we could check ourselves. Nobody had ever handed us evidence like that and asked us to sign it.
    Head of IT Wholesale distribution business
Questions

AI solutions: the questions we are asked

There is no honest fixed price, because the tasks are not comparable. Market rates give a reference: a full stack developer is quoted at PKR 3,500 to 8,000 an hour here, and custom application work is published at PKR 200,000 to 1,000,000 and above. Pakistani rates sit roughly 60 to 80 percent below Western equivalents. As an AI development company in Pakistan we scope and price a pilot on one process, then quote the build.

Reported timelines in this market run six to twelve weeks for a focused single scope go live, which for us is a measured pilot on one process, and three to six months once forecasting or several systems are involved. The usual delay is not engineering. It is assembling real cases with agreed correct answers, because in most organisations nobody has ever recorded them.

Not under the terms we work to. Commercial API agreements from the major providers exclude training on customer content, and we confirm the processing region and retention period in writing before any prototype touches real records. Logs and monitoring are covered by the same decision, since request content ends up there too and is frequently overlooked.

An API is enough for most enterprise work, including extraction, classification and grounded question answering. Training your own model earns its cost when volume is genuinely high, the vocabulary is specialised, latency or residency rules out a hosted call, or per unit economics beat the API. The cost profile then inverts: large up front, small per use, plus retraining.

The design assumes it will. Answers are grounded in your own documents with citations, extracted values are validated against purchase orders and master data, and confidence thresholds route uncertain cases to a reviewer rather than posting silently. Every automated action is logged so it can be traced and reversed, and a person stays in the loop wherever money moves.

Yes. Open weight models run on hardware you own, which keeps content inside your network and converts a metered bill into capital you have already spent. You then own the graphics capacity, drivers, model updates and the skills to maintain them. A regional deployment or private endpoint from a commercial provider often satisfies residency without the hardware.

One that is high volume, rule heavy, currently manual and checkable. Invoice processing is the usual first candidate because the volume is known, accuracy can be measured against what people do today, and the saving is arithmetic rather than opinion. If nobody can tell whether an output was right, that process is not a candidate however tedious it looks.

It can, where you sell into Europe or your output is used there, and obligations scale with the use rather than the technology. Employment screening and creditworthiness are treated far more strictly than reading a supplier invoice. That classification is a legal determination for your own advisers. We produce the technical evidence they require and do not offer the opinion ourselves.

Have a process that eats a week every month?

Describe it and we will tell you honestly whether AI helps, what it would cost to prove, and how you would measure whether it worked.

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