Khattak Solutions Infrastructure
AI & Automation
Every handoff that depends on someone remembering is a process waiting to fail.
Is this you?
- Skilled people spend hours a week on copy-paste between systems.
- Enquiries wait overnight because they arrive outside working hours.
- The same questions reach your team dozens of times a week.
- You have the data to make decisions and no time to assemble it.
If more than one of those is true, the problem is usually the system around the work rather than the people doing it.
What we automate
Lead routing, qualification, follow-up, data entry, document handling, reporting and internal request flows. We start with the process that costs the most hours and has the clearest rules, then extend from there.
Where AI belongs
AI is useful where language, classification or summarisation is the bottleneck — qualifying enquiries, answering from your own documentation, drafting responses for review. We ground assistants in your content and keep a human in the loop wherever a mistake would be expensive.
Automate the process before you add intelligence to it
A badly designed process does not improve when you put a language model on top of it. It becomes a badly designed process that is harder to audit.
We start by mapping where work actually queues — the handoffs, the rekeying, the waiting. A large share of what businesses want AI for turns out to be a scheduled job, a webhook and a decent data model. That work is cheaper, more reliable, and it is where the hours actually come back.
Where language models genuinely earn their place
- Qualifying and routing inbound enquiries by what they actually say
- Answering repeat questions from your own documents, with sources attached
- Summarising long threads, calls and documents into something a person can act on
- Extracting structured data from unstructured input — emails, PDFs, forms
- Drafting replies for a human to approve rather than sending unreviewed
Built with a human in the loop where it matters
Anything that touches a customer, a price or a commitment is built to propose rather than to act, until it has earned the right to act. Every automated decision is logged with its inputs, so when something goes wrong you can see why rather than guess.
We do not build systems that make irreversible decisions without a person.
Retrieval over your own knowledge
An assistant that answers from your actual documentation, contracts and product data — rather than from general training — needs a retrieval layer, chunking that respects document structure, and citations the user can check. We build that properly, because an assistant that is confidently wrong costs more than no assistant.
How the work runs
01 — Process mapping
Half a day watching the work. We come back with where the hours go, ranked.
02 — Shortlist
What to automate first, with estimated hours returned and build cost for each.
03 — First automation
One process, end to end, in production, usually in two to four weeks.
04 — Measure
Hours saved and error rate against the baseline, so the second one is an easy decision.
05 — Extend
The next process, now with the integration groundwork already laid.
What it costs
Automation is quoted per process, and the first one carries the connection work that the rest then reuse. We will not quote a programme before one process is live and measured.
What moves the number
- How many systems the process touches, and whether they have APIs
- Whether a human approves each outcome or only exceptions
- Volume, which drives model and infrastructure running cost
- Data quality going in — the most common hidden cost
- Audit and logging requirements
- Ongoing model and API usage, which is a monthly cost, not a one-off
Every proposal is a fixed price against a written scope. We do not quote by the hour for work we have specified.
What's included
- Custom AI business applications
- Customer service and sales chatbots
- RAG assistants grounded in your documents
- Workflow and process automation
- API development and integration
- CRM and pipeline automation
- Data processing and enrichment pipelines
- Reporting and analytics dashboards
Common questions
Will an AI assistant give customers wrong answers?
We ground assistants in your approved content, constrain what they're allowed to answer, and route anything outside scope to a person. Accuracy is a design decision, not a hope.
Do we need to replace our current tools?
Usually not. Most automation work connects tools you already pay for through their APIs.
What happens to our data?
It stays in systems you control. We define retention, access and processing terms in writing before any integration is built.
Where does our data go?
Wherever you decide, and we will tell you plainly for each component. Retrieval and storage can be kept in infrastructure you control. Where a third-party model is used, we tell you which, what it receives, and what its provider's retention terms are.
What if the AI gets it wrong?
Design assumption, not edge case. Anything customer-facing proposes rather than acts until measured accuracy justifies otherwise, every decision is logged with its inputs, and there is a route for a person to intervene.
Do we need AI at all?
Often not, and we will say so. A lot of what gets asked for as AI is a scheduled job and a webhook — cheaper to build, cheaper to run, and it fails in predictable ways.
How soon do we see a return?
The first automation should pay for itself in hours returned inside a quarter. If we cannot make that case from your numbers before we build, it is the wrong first process.
Have a project?
Let's build
what's next.
Whether you need customer acquisition, software, automation or communications infrastructure, we will design the right solution around your business.
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