Will the AI make things up about my business?

That is what grounding is for. We retrieve from your own content and require the model to answer from it, with a scored evaluation set proving the accuracy rate before launch and an escalation path for anything it should not answer.

How do we control the cost of AI features?

Model choice per task, prompt caching, retrieval that sends only the relevant passages instead of whole documents, and hard spend caps with alerting. Most of the saving comes from not using a frontier model for work a small one handles fine.

Can this run on our own infrastructure?

Yes, where it needs to. We can deploy open-weight models in your cloud for data-residency or compliance requirements, though hosted APIs are usually cheaper and better until volume is high.

Useful AI, not demos

AI Integration & Automation

AI integration adds language-model features to existing products: retrieval-augmented search over your own content, document data extraction, support deflection and internal copilots. MOMT builds these against the Claude and OpenAI APIs with evaluation and cost controls in place before launch.

Talk about AI See the work

01BuildSoftware that holds up after launch day.

Typical timeline: 3–8 weeks

Tools & stack

Claude API
OpenAI
Vercel AI SDK
pgvector
Pinecone
Node.js
Python

Outcomes

Measured, not guessed: Eval-first; Measured, not guessed
Grounded retrieval: Your data; Grounded retrieval
Token spend controls: Capped; Token spend controls

8 deliverables

What's included

LLM features that solve a real job — support deflection, document extraction, search, internal copilots — wired into the systems you already run.

Use-case assessment and feasibility check
Retrieval pipeline over your own content
Prompt and tool design with version control
Evaluation suite and quality baselines
Guardrails, fallbacks and human escalation
Cost monitoring and spend caps
Workflow automation and internal agents
Streaming UI for AI features

4 steps

How it works

Typical timeline: 3–8 weeks

01
Pick the right job
We start from a task with a measurable cost — minutes per ticket, hours per week of data entry — not from the technology.
02
Ground it
Retrieval over your documents and data so answers cite your content rather than inventing it.
03
Evaluate
A scored test set before launch, so you know the accuracy number instead of relying on a convincing demo.
04
Ship with guardrails
Fallbacks, escalation to a human, spend caps and monitoring — then iterate on the eval scores.