D I Z I T A L O G I C

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Dizitalogic is an AI first software company. We build agents, retrieval systems and the full stack products they run inside, for clients across six markets. Twenty plus engineers, working in your stack and your timezone. a digital-first world.

Contact Info

Plot# 3, Block J, Main Road, DHA 2, Islamabad
info@dizitalogic.com
Monday to Friday, 9am to 5pm
+92 321 1120222

AI agents and automation

Agents that run
the work, not demos

We build agent systems on OpenAI, Anthropic Claude and Gemini, orchestrated
with LangGraph and n8n, wired into the tools your team already uses.

See what we build Talk to an engineer

RAG and knowledge assistants

Your company knowledge,
answered in seconds

We train assistants on your documents, tickets and databases, then retrieve
against Pinecone, Weaviate or pgvector so the answers cite real sources.

Explore AI services Book a discovery call

Full stack engineering

From prototype to
production system

A team of 20 plus engineers building in Flutter, MERN, Next.js and Laravel,
delivering to clients in the USA, UK, Australia, Canada, Europe and the Middle East.

View our services Start a project

What we build

Six ways we put AI into your business

01 / AI agents

Agents and multi agent systems

Agents that plan a task, call your tools, hand work to each other and stop for human approval where the cost of being wrong is high.

OpenAI
Anthropic Claude
LangGraph
LangChain

Scope an agent build

02 / Automation

Workflow orchestration

We connect the systems you already pay for, then let an agent run the steps a person used to copy and paste between them.

n8n
Make
Python
Azure AI

03 / RAG

Knowledge assistants

Trained on your documents, tickets and databases, and built to cite the source it answered from so your team can check it.

Pinecone
Weaviate
pgvector
AWS Bedrock

Talk about your data

04 / Voice

Conversational and voice agents

Agents that answer on chat and on the phone, hold context across a conversation and pass to a person the moment they should.

OpenAI
Google Gemini
Mistral
Llama

Plan a voice pilot

05 / Vision

Computer vision

Reading documents, checking product photos, spotting defects on a line and turning what the camera sees into a record you can query.

PyTorch
TensorFlow
Python
AWS

Discuss a vision use case

06 / Forecasting

Predictive analytics

Demand, churn and risk models built on the data you already hold, delivered as an API and a dashboard your team will actually open.

Python
PostgreSQL
Azure AI
AWS Bedrock

Review your data

How it runs

Agents at work

A request arrives, the orchestrator decides who should handle it, agents call the models and tools they need, and the result comes back checked. This is the shape of most systems we build.

Requests routed

Tool calls made

Tasks completed

The numbers above are illustrative and animate to show how the flow behaves. They are not Dizitalogic performance figures.

Our Services

AI first, and the full stack around it

An engineer reviewing an agent run

AI Agents and Automation

Agents that plan, call your tools and stop for approval where it matters.

A data scientist inspecting model output

AI and Machine Learning

Forecasting, classification and vision models trained on your own data.

A knowledge base open on screen

RAG and Knowledge Assistants

Assistants trained on your documents that cite the source they answered from.

A support call being handled

Conversational and Voice AI

Chat and voice that resolve the call, then hand to a person cleanly.

A product team planning a release

SaaS Product Development

Multi tenant products with billing, roles and audit logging from day one.

Engineers working together at a desk

Team Augmentation

Named engineers inside your team, on your board, monthly rolling.

A developer reviewing code on a laptop

Web Development

Next.js, MERN and Laravel builds, shipped to production.

A mobile app being tested on a phone

Mobile Apps

One Flutter codebase for iOS and Android, native where it counts.

An online store open on a tablet

Shopify Store

Themes, checkout logic and the integrations behind the sale.

A team planning an app build at a desk

Shopify Apps

Private and public apps built to pass Shopify review.

A content editor working on a website layout

WordPress Development

Custom themes and blocks, no page builder bloat.

A packed order ready for shipping

WooCommerce

Catalogue, tax and shipping rules that match how you sell.

Code on screen during a plugin build

WordPress Plugins

Plugins written for one job, documented and handed over.

An analytics dashboard showing search performance

SEO

Technical fixes first, then content that compounds.

A social media campaign being planned

Social Media Marketing

Campaigns planned against a goal you can measure.

Talk through your build
with an engineer

Call Us Now

+92 321 1120222

How IT work

How an engagement runs

1

Scope the problem

We start with the workflow you want changed, not a feature list. One call, then a scope in writing.

Design the system

You see the architecture, the model choices, the data flow and a cost estimate before anyone writes code.

2

3

Build in short cycles

Working software every week, running on your own data, in a staging environment you can open yourself.

Ship and hand over

We deploy, write the documentation and stay on while your team takes the system over.

4

about company

AI first, and we still ship the whole product

Most agencies either build models or build software. We do both, which is why our agent work reaches production instead of stopping at a demo. The same team that designs the retrieval layer writes the Next.js front end it answers into.

AI Engineering

Full Stack Delivery

20+

Engineers

6

Markets Served

7

Core Services

How to work with us

Three ways to start

Project build

Suits you when the outcome is already clear

You know what needs building. We agree the scope in writing, then deliver it against fixed milestones.

Written scope and architecture before any code
Working software demonstrated every week
Fixed milestones, so cost is predictable
Documentation and handover at the end

Typically six to twelve weeks.

Dedicated team

Suits you when the roadmap keeps moving

Named engineers work as part of your team. You set priorities, we bring the people and the practices.

The same engineers month to month, not a rota
Your standups, your board, your repositories
Overlapping hours with your timezone
Monthly rolling, scale the team up or down

Minimum one month, no long lock in.

AI pilot

Suits you when you want evidence first

We take one workflow, build it properly on your own data, and you decide from something real rather than a slide deck.

One workflow, chosen for measurable value
Running on your data, in a staging environment
Evaluation results, not a demo script
A written recommendation on whether to go further

Two to three weeks, then a clear decision.

Not sure which fits? Describe the problem and we will tell you which one we would recommend, including when the answer is that you do not need us yet.

Talk to an engineer

Faq

Questions we get asked before a build starts

Two to three weeks for a working prototype on your own data. A production agent handling real volume is usually six to twelve weeks, depending on how many systems it has to reach into.

No. Your data is used to answer your queries and nothing else. Retrieval keeps your documents in a vector store you control, and we can run the whole stack inside your cloud account if your policy requires it.

We assume it will. Agents run against evaluation sets before release, high risk steps stop for human approval, and every tool call is logged so you can see exactly what the system did and why.

No. We build behind an abstraction so OpenAI, Anthropic Claude, Gemini, Llama or Mistral can be swapped per task. Most systems we run use more than one, picked on cost and accuracy for that step.

Bring us the workflow you want fixed

Talk to a Specialist