AI development company
Timeline Digital is an AI development company providing AI automation services for business: AI agents, document processing, chatbots and knowledge search, built into the systems you already run.
Since 2013: 1,500+ software projects for 860+ clients.
Most AI projects stall because they chase a demo instead of a task. We start with the one job the AI must do, build the pilot on a slice of your real data, and measure accuracy with an evaluation set before launch. You get a feature that grounds its answers in your data, knows when to say it is unsure, and is monitored once it is live.
Direct answer
AI automation for business puts language models and machine learning inside your existing workflows to read documents and emails, answer questions and prepare actions, while rules route the work and people approve what matters. Timeline Digital, in Islamabad since 2013, builds it on your own data and starts with a free pilot.
What every AI build includes
Our AI automation services cover AI agents that act in your ERP and CRM, document processing that reads invoices and forms, chatbots and assistants, knowledge search, forecasting and process intelligence. Each is delivered as a managed project: a Plan step, a free pilot of 2 to 3 key modules that you test, the build, and SLA support.
| Service | What it does | A good first job | Read more |
|---|---|---|---|
| AI agent development | Reads requests, documents and emails, looks up data in your ERP or CRM and prepares the next action for a person to approve | Supplier invoice intake, or order-status replies on email and WhatsApp | AI agent development |
| AI document processing | Reads invoices, forms and contracts, validates the fields against your rules and posts approved data to your ERP | Accounts payable invoices that are re-keyed by hand today | AI document processing |
| AI chatbots and assistants | Answers questions from your own documents on web chat, WhatsApp and internal tools, with citations and handover to a person | Repeated support, HR or IT questions | AI chatbot development |
| AI knowledge search | Lets staff search a company knowledge base in plain language and get sourced answers that respect permissions | Policies and manuals spread across drives and wikis | AI knowledge search |
| Forecasting and analytics | Projects demand, sales, cash or inventory from your history, back-tested and shown with ranges | A demand forecast compared with last year’s figures | AI forecasting |
| Business process intelligence | Rebuilds how work really flows from system timestamps and shows where cases wait or loop | Delays in order-to-cash or purchase-to-pay | Business process intelligence |
| Workflow automation with AI steps | Routes approvals and notifications with rules, adding an AI step where a document or message has to be read | An approval flow with an AI step that reads the attachment | Workflow automation |
The AI projects that return value are narrow and specific. Here are the four patterns we build most, because they solve a measurable problem instead of chasing a headline.
A support or internal assistant that answers from your help docs, policies or product manuals through RAG, with citations and a clear "I do not know" when the answer is not in the source. It can reduce repeat tickets and gives staff one place to ask.
Read invoices, contracts, forms or resumes and pull the fields you need into structured data your systems can use. The model returns validated JSON against a schema, and low-confidence extractions get flagged for a human instead of silently going wrong.
Route, summarise, draft and classify the work your team handles by hand: triaging emails, drafting replies, summarising long threads, tagging records. The AI handles the routine pass and a person reviews the edge cases. The approvals and routing around it are covered on our workflow automation page.
Search that understands intent, content generation, recommendations or an in-app copilot. We build the AI as one part of a real product, with the streaming, rate limits and usage controls a live feature needs.
AI agent development builds software that reads a request, document or message, looks up data in your ERP or CRM through tools you allow and prepares the next action, such as a draft invoice or a customer reply. A person approves anything that changes data or reaches a customer, and every step is logged.
An agent project starts with one workflow and a free pilot of 2 to 3 key modules that you test before deciding on the final agreement. See AI agent development for the scope, the approval controls and the audit trail, or AI agents vs RPA vs workflow automation to check whether you need one.
Use rules-based automation when the steps are predictable, an AI chatbot or assistant when people ask questions your documents can answer, an AI agent when the work needs lookups and actions across your systems, and document AI when staff re-key documents by hand. Many systems combine them: the AI reads, answers or prepares, and rules route the result.
| Question | Rules-based automation | AI chatbot or assistant | AI agent | Document AI |
|---|---|---|---|---|
| What it does | Follows fixed if-then rules: routes approvals, sends notifications, moves data between systems | Answers questions and drafts text from your own documents and records, with citations | Completes a multi-step task: reads a request, looks up your ERP or CRM and prepares or takes the next action | Reads invoices, forms or contracts and extracts the fields into structured data |
| Best for | Steps that are predictable and can be written down as rules | Questions staff or customers ask again and again, where the answer is in your content | Repeated back-office work that needs reading and judgment across several systems | High volumes of documents that are re-keyed by hand today |
| Messy or varied input | Not handled: anything outside the rules fails or waits for a person | Handled for free-text questions, within what your content covers | Handled for emails, documents and messages; uncertain cases stop and go to a person | Handled for varied layouts, with low-confidence fields flagged |
| How accuracy is checked | Tested like any software: each rule fires correctly or it does not | An evaluation set of real questions with agreed answers, re-run on every change | Past cases with known outcomes, re-run on every change, plus a log of every tool call | Extractions scored against documents your team labelled, and values checked against your rules and ERP |
| Where a person stays in the loop | Exceptions the rules do not cover | Low-confidence answers, and anything that reaches a customer or changes a record | Approving any action that changes data, moves money or reaches a customer | Fields below the confidence threshold and mismatches with your records |
| Running costs | Hosting and monitoring; no model usage | Model usage, indexing and the vector database, plus monitoring | Model usage per step, hosting, monitoring and reviewers’ approval time | Model usage per document, hosting, and the time spent reviewing flagged items |
| Read more | Workflow automation | AI chatbot and assistant development | AI agent development | AI document processing |
Match the tool to the input. Predictable steps and data suit plain workflow rules, which are cheaper and easier to test. RPA can bridge a system with no interface. An AI step reads a document, email or question. An agent prepares the next step across systems, and a person approves it.
| Process | Plain workflow rules | RPA (screen robots) | AI step inside a workflow | AI agent |
|---|---|---|---|---|
| Approve a purchase request above a set limit | Best fit: the limits and approvers are fixed | Not needed if the system has an API | Only to read an attached quote | Poor fit: rules already do the job |
| Copy data between two systems when one has no API | Fits when both sides have an interface | Best fit as a stopgap, but breaks when screens change | Not needed | Poor fit: slower and costlier than a robot |
| Supplier invoices arriving as PDFs in many layouts | Cannot read the documents | Cannot read varied layouts | Best fit: extract, validate against your rules, review low-confidence fields | Only for exceptions that need a lookup |
| Staff asking where the answer is in policies and manuals | Not suited to free-text questions | Not suited | Best fit: answer from approved sources with citations | Only if the question must trigger an action |
| A customer email about a late order that needs checking in the ERP and a reply drafted | Cannot interpret the email | Cannot interpret the email | Classify and draft; a person does the lookup | Best fit: reads, looks up, drafts; a person approves the reply |
| Month-end matching of two ledgers on exact keys | Best fit: exact rules, fully testable | Works if no integration exists | Only for the fuzzy leftovers | Poor fit: needs no judgment |
| A report sent on a schedule with fixed content | Best fit | Overkill | Only to add a written summary | Overkill |
The reasoning behind each row is in our guide to AI agents vs RPA vs workflow automation, and the first process is best chosen with the first-project scoring matrix. Non-AI approvals, routing and notifications are covered on our workflow automation page.
Many teams already use ChatGPT and want the same thing working on their own information: price lists, policies, contracts, product specifications, past tickets and the records in their ERP or CRM. Pasting that into a public chat tool is slow and sends your data somewhere you do not control.
We build a private assistant, often called a custom GPT, that answers from your own content through retrieval augmented generation (RAG). Your documents and records are indexed, the passages relevant to each question are retrieved, and the model answers from them with citations, or says it does not know. See AI knowledge search and AI assistants and chatbots for the full scope.
AI document processing reads supplier invoices, purchase orders, delivery notes, forms and contracts, extracts the fields you need into a fixed schema, checks them against your rules and ERP data, and sends anything below a confidence threshold to a person.
Approved data is posted into your accounting or ERP system with the original document linked, so every figure can be traced back to its source. See AI document processing for the full scope, or AI agent development when the next step should be prepared automatically too.
Documents arrive from email, upload, a scanner or another system.
The model identifies the document type and pulls out the fields.
Values are checked against your rules and live ERP data.
Low-confidence items and exceptions wait for a person.
Approved data is posted, with the original and an audit trail kept.
The pilot is free and runs on a slice of your real data. It answers the questions that decide whether to continue, and it is honest about the ones it cannot answer yet.
Before the pilot, we build an evaluation set with your team: real questions or documents from your organization, each paired with the answer or fields a person agrees are correct. Every model, prompt or retrieval change is scored against it, so accuracy is a number you can compare over time rather than an impression from a demo. The set grows as production surfaces new cases, and a change that lowers the score is brought to you before it goes live.
Estimate the return from the time and errors the automation removes, minus what it costs to run. Monthly benefit = hours saved × loaded hourly cost + errors avoided × cost per error. Payback in months = one-time build cost ÷ (monthly benefit − monthly running costs). Record the baseline before the pilot, so the after-figures are measured.
Record the baseline before the pilot: monthly volume, minutes per item, and the error or rework rate.
Measure the after-figures in the pilot, on your own data: minutes per item including human review, and the error rate on the evaluation set.
Value the difference: hours saved × the loaded hourly cost of the people doing the work, plus errors avoided × the cost of fixing one.
Subtract the monthly running costs: model usage, hosting, monitoring and evaluation upkeep.
Divide the one-time build cost by the net monthly benefit to get the payback period in months.
The figures below are illustrative assumptions to show the method. They are not a client result, a benchmark or a quote; your pilot replaces them with measured numbers.
| Illustrative figure | Before | After | Difference |
|---|---|---|---|
| Supplier invoices per month | 2,000 | 2,000 | No change |
| Handling time per invoice | 6 minutes (200 hours a month) | 2 minutes, including review of flagged items (about 67 hours) | About 133 hours saved |
| Invoices needing rework (30 minutes each) | 3% (60 invoices, 30 hours) | 1% (20 invoices, 10 hours) | 20 hours saved |
| Value at an assumed loaded cost of $30 an hour | About 153 hours × $30 = about $4,600 a month | ||
| Net monthly benefit | About $4,600 minus the monthly running costs | ||
| Payback period | One-time build cost ÷ net monthly benefit |
We map where your data goes before any code is written, then pick from these options together. We design to support the data-protection rules that apply to you; compliance remains your organisation's responsibility, confirmed by your own assessment.
How we handle client data during delivery is set out on our security and data protection page.
These depend on your volume and the model you choose, so we list categories rather than amounts.
The review step has its own screen, queue and audit log. It is part of the design, not an afterthought.
Custom software built around the way your business works. In the Pilot you see the AI answer on a slice of your real data before the full build, and Build & Scale adds the guardrails, evaluation and monitoring a live feature needs.
We learn how your business works.
Your requirements, workflow, challenges and goals, understood before anything is recommended.
We design the right solution around your workflow.
Modules, workflows, roles, approvals, reports and integrations, agreed before development.
Choose the right technical foundation.
Technology options matched to your users, security, budget and growth, not one fixed stack.
Test our work before full project development.
Free. You choose 2 to 3 key modules and we build them first, so you can judge our work.
The full project starts only after you approve the pilot.
From approved pilot to complete digital system.
Full development, testing, deployment, training and support, built to grow with you.
Step 1
Pick one process with a named owner. Record the baseline before anything is built: monthly volume, minutes per item, error or rework rate, and what a correct result looks like. Collect real cases, including the awkward ones.
Step 2
Decide which steps are rules, which need AI and which stay with people. Write down what the AI may read and what it may do, where a person approves, and where the data goes. The pass and fail thresholds for the pilot are agreed here.
Step 3
Choose the model and hosting per task by scoring the options on your own cases, within your data rules: a commercial API with zero-retention settings where offered, regional hosting, or a self-hosted open model.
Step 4
We build 2 to 3 key modules free of charge, ready in about 2 days, and run them on a slice of your real data so you see actual answers and failures. One department or one process is a valid pilot scope.
Step 5
After you approve the pilot: integrations with your ERP, CRM or finance system, guardrails, the review queue, logging, monitoring and an evaluation set that is re-run on every change. Long-term SLA support follows launch.
Claude, GPT and open models through one provider-agnostic layer, so you are not locked to a single vendor and can route simple requests to smaller, faster models. Model choice is a config value, not a rewrite.
Retrieval augmented generation over your private documents using pgvector, Pinecone or Qdrant. The model answers from your data with citations, instead of guessing from training data it never saw.
Input and output validation, prompt-injection checks, PII redaction and structured output schemas so the model returns clean JSON your code can trust, not free text you have to parse.
LangChain, LlamaIndex or plain typed code for multi-step chains, tool calling and agent workflows. We pick the simplest thing that ships and avoid framework lock-in where the logic is small.
A labelled test set and automated scoring so accuracy is a number you can track across model and prompt changes. Without evaluation, every prompt tweak is a guess.
NestJS on Node.js or ASP.NET Core for the API, Next.js for the interface, streaming responses and a job queue for long-running document processing.
Self-hosted open models, or API settings with zero retention where the provider offers them, when your data cannot leave your control. We map the data path before building so your compliance requirements shape the design.
Timeline’s own engineers build the AI feature into your production systems as a managed project, with one project lead who keeps the build aimed at the one task that returns value.
Each area has its own page with scope, process and FAQs. Start where your priority is.
Assistants that answer questions and handle work from your data.
Agents that read, look up and act in your ERP and CRM, with human approval.
Pull data out of documents and forms automatically.
Our workflow automation page: approvals, routing and notifications, with AI-assisted steps such as classifying or extracting data where they help.
AI-supported insight for planning, trends and performance.
Make your internal knowledge instantly searchable.
Use AI signals to see and improve how work really flows.
In-depth, practical reading for teams planning this kind of project.
An AI development company builds software that uses machine learning and large language models to do real work: answering questions over your documents, automating manual tasks, extracting data and running chatbots on private data. The work covers data pipelines, retrieval, guardrails and evaluation, not just an API call. Timeline Digital ships these features to production with accuracy measured, not assumed.
AI development services cover choosing the task worth automating, preparing your data, building the AI feature, wiring it into your systems, then measuring and maintaining it. At Timeline Digital that means AI agents, AI chatbots and assistants, document processing, knowledge search and forecasting, each with guardrails, an evaluation set and human review.
An AI chatbot answers questions in a conversation, from your documents, and hands over when unsure. An AI agent completes a multi-step task: it reads a request, looks up your ERP or CRM data through tools you allow and prepares the next action, with a person approving changes. Choose a chatbot to answer and an agent to act.
Timeline Digital does not publish prices for custom software, because every project is custom. The price is set after the free pilot, once you approve it. Model usage and hosting are running costs in your own accounts. Our guide to generative AI use cases and what they cost shows typical market ranges.
Ask each company to show the AI working on a sample of your own data, not a demo on theirs. Then check how they measure accuracy, where your data goes, how uncertain cases reach a person, and who owns the code, prompts and evaluation set. Our guide on how to evaluate an AI pilot gives a go/no-go scorecard.
We work with commercial model APIs such as Claude and GPT and with open-weight models hosted in your own cloud account, through one provider-agnostic layer, and choose per task by scoring the options on your test set. We claim no partner status or certification with any model provider, and third-party models stay under the provider’s terms.
Yes. Timeline Digital delivers remotely from Islamabad, with office hours of Monday to Friday, 9:00 to 18:00 Pakistan time (PKT, UTC+5). Those hours overlap UK and European mornings and the Gulf working day, and calls with US and Canadian teams are booked at the start of their day. There is no office outside Islamabad.
There is no fixed timeframe: the schedule is set from your requirements in the Plan step. Timeline Digital builds the free pilot of 2 to 3 key modules, ready in about 2 days, with its own engineers as a managed project. You test it, and the full build starts only after you approve it. SLA support follows launch.
Yes. We map the data path before writing code. When data cannot leave your control, we use self-hosted open models, or commercial APIs with zero-retention and no-training settings where the provider offers them. We add personal data redaction, role-based retrieval and audit logging, and design to support your data-protection obligations; compliance remains your organisation’s responsibility.
A pilot shows whether the model can do the task on your real documents or questions, at an accuracy measured on a labelled test set, where it fails, and what your data needs before production. It does not prove accuracy on cases outside the sample, behaviour at full volume, or business outcomes such as time saved, measured after rollout.
Compare what the task costs before and after. Record the baseline first: monthly volume, minutes per item and error rate. The monthly benefit is hours saved times the loaded hourly cost, plus errors avoided times the cost of fixing one. Subtract running costs, then divide the one-time build cost by the net monthly benefit to get the payback period.
Use plain workflow rules when steps and inputs are predictable, RPA only as a stopgap where a system has no API, an AI step when a document or message must be read, and an AI agent when the work needs lookups and actions across systems, with a person approving. See AI agents vs RPA vs workflow automation.
Published by Timeline Digital, a custom software development company founded in Islamabad in 2013. The facts on this page (track record, schedule, pricing policy and ownership terms) come from the same company facts used across the site. No accuracy figure is promised before a pilot on your own data, and no model-provider partnership or certification is claimed.
Author
Usama Asif
CEO and CTO, Timeline Digital. Founded the company in 2013 and leads its software and AI delivery work.
Bring us the task and a sample of the data it has to read. We will tell you honestly whether AI is the right tool, agree the first modules with you, and build the pilot on your real data first. The project code, prompts and evaluation set transfer to you on full payment.
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