Services / AI and automation

AI that does the work, not the demo.

Q: Which model do you use?

Whichever scores best on your data within your cost and privacy limits. We test two or three before recommending one.

Q: Will our data train someone else’s model?

No. We use API terms that exclude training, or self-hosted models when the data is sensitive. The data flow is written into the scope.

Q: What if the AI gets it wrong?

Errors are managed, not wished away. Every system has confidence thresholds, a human review path and accuracy tracked over time.

Q: How do you keep running costs predictable?

We project token costs during scoping, then add caps and per-feature monitoring in production.

Q: Is this the same as OMKA.AI?

Yes. OMKA.AI is our AI division. This page covers custom work; the OMKA.AI page covers the ready modules such as Digital Mail.

We put AI inside the workflows your team already runs: reading documents, matching records, routing work and drafting replies. Every system has a human checkpoint and a measured accuracy.

Talk to an engineer →See the work

Where AI pays for itself

Not every process needs a model. These are the ones that usually do.

Staff retype data from scans, PDFs, labels or emails into another system.
Someone spends hours a day matching documents to the right customer, order or account.
Your team answers the same questions from the same documents every week.
Leads, tickets or requests wait in an inbox because nobody has time to triage them.
You tried a chatbot once and it made things up. You want one that cites its sources.

What we automate

Delivered through OMKA.AI, our AI division, and plugged into the platforms we build and the ones you already run.

Document intake

OCR and extraction for mail, invoices, forms and labels, straight into your system.

Running at Expansive Digital Mail

Reading photos and forms

A photo of a traffic ticket becomes a filled-in case an attorney can work, with the court timeline tracked.

Running at Fix My DL

Invoice and bank reconciliation

Bank lines and invoices matched to ledgers, TDS worked out and vouchers posted to Tally, with an accountant approving exceptions.

Running at OMKA.AI finance tool

Workflow agents

Multi-step jobs like lead follow-up or shipping-document checks, with tool use and escalation to a person.

For freight, insurance and agri clients we have consulted

Search over your documents

Answers grounded in your policies, contracts and manuals, with the source shown.

LLM features in your product

Summaries, classification and drafting inside the screens your users already know.

Mail for 40+ locations, read in under a minute.

Expansive receives mail and parcels for members across more than 40 US coworking locations. Our pipeline scans each item, reads and matches it with Claude, falls back to address matching and hands anything uncertain to a person.

95%+Items matched automatically

<60sFrom scan to member

40+Locations live

Read the Expansive case study →

How an engagement runs

The same people stay with you from the first workshop to launch day. No handoffs between strategy, design and engineering.

Weeks 1–2

Use case and feasibility

We define the job, the success measure and the data, then check it is doable before you commit.

Use case definition
Accuracy target agreed
Model shortlist
Fixed-scope proposal

Weeks 2–4

Prototype on real data

A working prototype on a sample of your real documents, with accuracy measured, not estimated.

End-to-end prototype
Evaluation set from your data
Measured accuracy
Go or no-go

Weeks 4–10

Production build

Hardened pipeline with logging, cost limits, retries and a human review path.

Integration with your systems
Logging and monitoring
Cost caps per feature
Human-in-the-loop review

Weeks 10–14

Launch and tune

Real traffic, real corrections, and prompt and retrieval tuning from what the reviewers fix.

Production launch
Accuracy tracked weekly
Prompt and retrieval tuning
Retainer or handover

Time and scope

6–14 weeks

Typical timeline

A focused feature such as extraction or classification ships in 4 to 6 weeks. Multi-step agents or document search run 10 to 14.

Data, accuracy, volume

How messy the input is, how accurate the output must be and how many items a day it must handle.

Measured before built

You see accuracy on your own data at the prototype stage. If it does not clear the bar, you stop there.

What you can hold us to

Production, not pilots

Our mail pipeline runs every day for a business with 40+ locations. We build for the hundredth item, not the first.

Model-agnostic

Claude, GPT, Gemini or an open model you host yourself. We benchmark on your data and pick on accuracy, cost and privacy.

A human stays in the loop

Low-confidence results go to a person with the evidence attached. The system learns from what they correct.

Your data stays yours

Enterprise API terms that exclude training, or self-hosted models for sensitive data, documented in the scope.

The stack

Picked during scoping, based on your systems and who will maintain the product after us.

Models

Chosen per task.

ClaudeGPTGeminiLlama

Document AI

Reading the real world.

Claude visionExtractionClassification

Retrieval

Grounded answers.

pgvectorPineconeLangChainLlamaIndex

Runtime

Where it runs.

AWS BedrockAzure OpenAISelf-hostedQueues

Questions we get asked

Which model do you use?