Engineering service

AI Application Development

Build production AI applications around defined workflows, reliable evaluation, controlled data access, and measurable usefulness.

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Who this is for

Built around a clear operating need

Organizations with a concrete knowledge, document, search, classification, or automation workflow.

Common problems addressed

Unstructured information that is hard to use
Manual classification and extraction
AI prototypes that cannot be evaluated
Uncontrolled cost, latency, or data exposure

What the engagement delivers

Use-case and risk assessment
Evaluation dataset and quality criteria
Production AI application
Monitoring, fallback, and cost controls

Technical capabilities

Retrieval-augmented generation
Document AI
LLM integration
Embeddings and semantic search
Model evaluation
Python
Human review workflows

Delivery approach

Architecture connected to delivery

We test whether rules, search, classifiers, or language models fit each step, then design evaluation and operational controls before scaling usage.

Engagements can begin with a focused assessment, continue as a defined project, or support an internal team through technical advisory and delivery oversight.

Relevant work

Related case studies

Manufacturing intelligence platform

Manufacturing Product Data Intelligence

A production data workflow that extracts, normalizes, enriches, and indexes inconsistent manufacturing product information for reliable search and classification.

Read the case study

Service questions

Frequently asked questions

Who is ai application development for?

Organizations with a concrete knowledge, document, search, classification, or automation workflow.

What does a ai application development engagement deliver?

Typical outputs include use-case and risk assessment, evaluation dataset and quality criteria, production ai application, monitoring, fallback, and cost controls. The final scope follows the system constraints and intended outcome.

Engagement models

Focused assessment, project delivery, or advisory

We scope the model around the decision or outcome required. Budget and timeline are discussed after the system context, constraints, and responsibilities are clear.

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