AI development

AI belongs in the backend.

Most AI is a chat window added on top. We put it where the work already happens: in the records, the jobs, and the decisions, inside your private system.

It drafts, sorts, checks, and summarizes in the background. People approve what matters.

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Where the AI sits

Inside the work, not beside it.

Reads the record

Summarizes a job, a customer, or a case from the data you already have. No copying it into a chat window.

  • Retrieval
  • Vector search
  • Amazon Bedrock Knowledge Bases
  • Azure AI Search
  • pgvector

Drafts the next step

Quotes, replies, follow-ups, and reports, held for a person to approve before anything goes out.

  • Claude
  • GPT
  • Gemini
  • Approval queues

Sorts what comes in

Email, forms, uploads, and tickets are classified and routed to the right person the moment they land.

  • Classification
  • Amazon SQS
  • EventBridge
  • Azure Service Bus

Pulls data from documents

Invoices, contracts, intake forms, and photos become fields in the record instead of attachments nobody opens.

  • Amazon Textract
  • Azure Document Intelligence
  • Google Document AI
  • Vision models

Watches the numbers

Flags anomalies, late jobs, and risk before someone has to go looking for them.

  • Forecasting
  • Anomaly detection
  • SageMaker
  • Databricks

Answers inside the product

An assistant that only knows your data and only shows each user what their role is allowed to see.

  • Role-scoped retrieval
  • Guardrails
  • Audit logs

Any model

The right model per task. Your data stays yours.

We do not marry one vendor. Each task gets the model that fits its cost, speed, and sensitivity, and the choice can change as the market does.

Models run through your own cloud account, so prompts and records are not used to train someone else's product.

  • Frontier modelsWhere reasoning, writing, or judgment matters.
  • Fast, low-cost modelsFor high-volume sorting, tagging, and extraction.
  • Private and open modelsWhen the data cannot leave your environment.

How we build it

Start with one task. Prove it. Then scale.

01

Find the hours

We find the one decision or task that eats the most time.

02

Wire it in

The AI runs in the backend on real data, with a person approving the output.

03

Measure it

Accuracy and time saved are tracked before it runs on its own.

04

Control the spend

Usage and cost are visible per feature, so the bill never surprises you.

Consultation

A free hour to see where AI fits.

Schedule a one-hour consultation with Dallas Mobile. We will come on site and run a proper whiteboarding session on the operation, or meet virtually if that is the better room. You leave knowing which tasks are worth automating and which are not. No fee.

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