How accurate is this, really?

It depends entirely on the task, so we measure it before we ship. We build an evaluation set from your own cases, report the pass rate on it, and put a human approval step in front of anything that moves money or goes to a customer.

What happens to our data?

We deploy on your infrastructure, or on ours if you prefer. On hosted models we use the enterprise plans where your data is not used for training. We sign NDAs and data processing agreements before any of your data is touched.

How do the running costs behave?

Model cost tracks token usage, so it scales with volume. We cache repeated calls, route easy steps to cheaper models, and show you the per-run cost in the dashboard so a bill is never a surprise.

Will it work with the tools we already use?

If it has an API, yes. For an in-house system with no API, we build the integration layer as part of the work.

AI automation that survives contact with your business

We build agentic workflows on LangChain and LangGraph, with retrieval over your own documents and data. The systems stop for a human where they should, and every run is recorded.

For teams whose process lives in inboxes and spreadsheets. Three to six weeks, typical.

Agent run, example

Invoice workflow

Steps

09:14:02Supplier invoice read, 3 line items extracted
09:14:05Matched to the purchase order, 2 of 3 lines agree

Line 3 is PKR 12,000 above the purchase order. Paused for a person to decide.

09:31:40Approved by the accounts manager, then posted to the ledger

Every step is recorded with its input, output and cost.

What we build

3 layers. Most projects use all of them, and none is worth much on its own.

Agentic workflows: Multi-step workflows on LangChain and LangGraph that hold state, call your systems, and stop for a human when the decision is expensive.
Retrieval over your data: A RAG layer over your documents, tickets and database, so a question in plain language is answered from what your business actually holds.
Model integration: Language models wired into the systems you already run: your ERP, your CRM, your inbox. No second place for your team to go and check.

Where it earns its keep

The work we are asked for most often.

Document processing: Pull structured fields out of invoices, contracts and forms, then route them into the workflow that acts on them. No retyping.
Customer support: Support agents that read your knowledge base, answer what they can answer, and escalate the rest with the context attached.
Analysis and reporting: Ask a question about your data in plain language, in Urdu or English, and get the answer out of the live database.
Sales and marketing: Lead scoring against your own closed-won history, drafted outreach a person still approves, and follow-up that reacts to replies.

Running in production

Automating one manual client onboarding flow took it from 2 hours to 25 minutes.

Bonnet.ai, brand work from a brief

A brand development platform: a creative brief goes in, and research, strategy, creative direction and moodboards come out as one document. Bonnet.ai is a venture we co-own and build. Every step is tracked on the server, streams its progress, and can be cancelled or rerun on its own.

Read the write-up

Obelisk, one assistant for marketing teams

More than 15 specialised agents behind one assistant, from SEO and email to brand voice and strategy. They work from a team's own documents, with every workspace's data kept apart. Parallel retrieval cut the wait for a first answer from 22s to 8 to 10s.

Read the write-up

Anatomia, nurse callbacks and voice follow-up

Call transcripts are scored for urgency so the urgent case is read first, and an outbound voice assistant follows up and writes what it hears back to the case.

Read the write-up

Questions in Urdu or English, in Polaris

Shop owners ask about their own stock and sales in the language they think in. The answer is rows out of the live database, and a question never writes.

See Polaris

What you get at handover

You should be able to fire us and keep the system running. This is what makes that true.

The code, and it is yoursFull source, in your repository, under your licence. Prompts, tools and evaluation cases included, so the system can be changed without us.
Written process documentationWhat each workflow does, what it costs to run, where it hands off to a person, and what to do when it fails. Written for your operators.
Traces and a run dashboardEvery run recorded in LangSmith with inputs, outputs and cost. When output looks wrong you can open the exact run and read it.

How the weeks go

A typical engagement runs three to six weeks, with a working demo at the end of every week.

Weeks 1-2
Map the process
We sit with the people doing the work and write down each step, with the time it takes today and who signs it off.
Weeks 3-4
Build and test
Workflows built against real cases from your business, reviewed with you weekly. You see failures as well as successes.
Weeks 5-6
Deploy and hand over
Live on your infrastructure or ours, your team trained on it, and a written runbook for the first month of operation.

The stack we run in production

What each piece does in the systems we run. We are not affiliated with any of these vendors.

The stack we run in production, by layer
Layer	What we use
Orchestration	LangChain, LangGraph
Observability	LangSmith traces, cost and latency per run
System of record	PostgreSQL
Hosting	Google Cloud, AWS, or a server you own

How we build agent systems, written up

Questions we get asked about automation

Bring us the process that eats your week.

A 30 minute call. We walk the process with you and name the first workflow we would build.

Book a call

First reply: same working day
Hours: Set to your time zone
Built in: Lahore, Pakistan

WhatsApp+92 335-0706014

AI automation that survives contact with your business

What we build

Where it earns its keep

Running in production

Bonnet.ai, brand work from a brief

Obelisk, one assistant for marketing teams

Anatomia, nurse callbacks and voice follow-up

Questions in Urdu or English, in Polaris

What you get at handover

How the weeks go

Map the process

Build and test

Deploy and hand over

The stack we run in production

How we build agent systems, written up

Multi-agent LLM middleware: lessons from Obelisk

Scoring fraud in legal intake calls

Structured logging for AI debugging

LangGraph vs traditional workflow engines: when to choose agentic AI

Questions we get asked about automation

Bring us the process that eats your week.