AI Integration
Practical AI systems with evidence, guardrails, and cost controls.
We integrate artificial intelligence capabilities into existing software products — focusing on practical business utility, deterministic guardrails, cost controls, and fallback mechanisms.
Why this exists
The problem it solves
Hype-driven AI projects often fail due to hallucinations, lack of private data grounding, unmonitored API costs, and missing human oversight.
RAG architecture and tool-calling models allow embedding real utility into software products when paired with evaluation guardrails.
Written scope
What is in scope, and what is not
AI outputs producing hallucinations, private document leakage risks, and runaway LLM token costs.
- Products adding grounded document search (RAG) over private knowledge
- Automating structured data extraction from PDFs and free text
- Deploying targeted AI assistants with tool access and fallback rules
- Expectations that AI models can operate flawlessly without human fallback on critical decisions
- Treating 'AI-powered' as a marketing buzzword rather than a specific product feature
- Document chunking & embedding pipeline
- RAG vector search setup
- System prompt & policy engineering
- Human fallback workflow
- Third-party LLM token fees (paid directly to OpenAI/Anthropic/Pinecone)
- Sample knowledge documents / data sources
- LLM API keys (OpenAI/Anthropic)
- Expected accuracy criteria
- Data source complexity and ingestion volume
- Custom tool integration and evaluation benchmarks
Capability inventory
What can be built
Decision framework
Should This Workflow Use AI?
Evaluate whether AI is truly required or if traditional software is better.
Document RAG & SearchRecommended default
Ground LLMs in private documents so answers cite real company knowledge.
If deterministic code can solve the problem, we will recommend it over AI to save you token costs and eliminate hallucinations.
Engagement sequence
How the work runs
- Use-Case & Cost AuditDetermine if AI is the optimal solution or if deterministic software is cleaner and cheaper.
- Data Pipeline & Embeddings SetupIngest, chunk, and embed knowledge documents into vector stores.
- Model Orchestration & GuardrailsConnect OpenAI/Anthropic APIs with strict system prompts, tool schemas, and output validation.
- Evaluation & MonitoringTest response accuracy across benchmark queries and monitor token costs.
Before and after
What actually changes
Delivery standards
The quality bar
Questions
AI Integration questions
Who pays for the OpenAI / Anthropic API tokens?
Next step
Scope AI Feature
Discuss your AI use-case with us for an honest technical assessment and cost plan.