AI Implementation
We build AI tools for specific jobs: searching internal documents, processing intake forms, and drafting responses for review. They connect to the software your team already uses.
We start with examples of the task, test models against them, and work out where human review is needed. Cost, response time, and accuracy all matter when choosing what to build.
What we build
LLM assistants and agents
Assistants that find information in your records and agents that carry out a defined set of actions. We specify what they can access, what they can change, and when they need approval.
RAG over your own data
Search across your documents, tickets, and records, with answers linked to their sources. We test retrieval and answer quality against questions your team needs to ask.
Document and intake processing
Extract fields from invoices, forms, emails, and PDFs, then send them to your other systems. Uncertain or incomplete results can go to a person for review.
Evaluation and guardrails
Tests built from examples of your task, including cases the model is likely to get wrong. We measure errors and set limits on the actions the system can take.
How the engagement works
We choose one task and agree on how to judge the results. Then we test it on representative examples before connecting it to your systems. Those results determine whether to proceed, adjust the approach, or stop.
- Choose a task and collect examples to test
- Connect the documents and records the task needs
- Measure accuracy, cost, and response time
- Set access limits, document the system, and train your team
Who this is for
For teams spending time searching internal documents, sorting incoming requests, or moving information out of forms and PDFs. We can assess which parts are suitable for AI and which need human review.
Common questions
How do you keep the AI from making things up?
Retrieval and source citations help, but they don't eliminate errors. We test answers against known examples, track failure cases, and add human review where mistakes would be costly.
Which model or provider do you use?
We compare models on examples of your task, looking at accuracy, cost, and response time. We also review your hosting and data-handling requirements before choosing a provider.
Related services
Data Engineering helps organize the records an AI tool needs. Workflow Automation connects its outputs to the next step in your process.
What would you like AI to help with? Send us a description of the task and how your team handles it today.