AI features that earn
their place in your product.
We don't add AI because it's trending. We integrate LLM capabilities, build RAG pipelines, and automate workflows where they solve a real problem — and we build them to production standards, not demo standards.
/ what's included
What we cover.
- LLM integration (OpenAI, Anthropic, Groq)
- RAG pipelines with vector databases
- Prompt engineering and evaluation frameworks
- AI-powered document processing and extraction
- Workflow automation and agentic systems
- Cost monitoring and latency optimization
/ deliverables
What you leave with.
- Production AI features integrated into your stack
- Evaluation framework for model quality
- Cost and latency benchmarks
- Prompt versioning and management system
/ process
How we approach it.
Problem scoping
Most AI failures start here. We spend time understanding what 'good' looks like, what the failure modes are, and whether AI is actually the right tool. Sometimes it isn't, and we'll tell you.
Prototype & evaluate
Build a fast prototype, define evaluation metrics, test it against real data. We don't ship AI features we can't measure — without evals, you're flying blind.
Production engineering
Move from prototype to production: input validation, output parsing, error handling, rate limiting, fallback logic. AI features fail in different ways than regular code — we build for that.
Cost & latency optimization
Token budgets, caching strategies, model selection and routing. LLM costs can surprise you at scale. We build with cost visibility from the start.
Monitoring & iteration
Prompt drift, model updates, accuracy degradation — these happen. We instrument for them and have a plan when they do.
/ stack
Tools we reach for in ai & automation engagements.
/ fit
Right fit.
This engagement works well when…
- You have a specific, well-defined problem that AI is demonstrably good at — classification, extraction, generation, or summarization
- You're adding AI to a production product that needs reliability, not just a demo
- You've tried a prototype and need someone to make it production-grade
Might not be the right fit if…
- You want AI because it's on the roadmap but haven't identified the specific problem it solves
- You need real-time audio/video AI — computer vision or speech recognition at scale are specialized engagements
/ faq
Common questions.
Which LLM should I use — OpenAI, Anthropic, or Groq?+
Depends on the use case. GPT-4o is the general-purpose workhorse. Claude excels at long-context tasks and nuanced instruction-following. Groq is the choice for latency-sensitive features. We often run multiple models and route based on the request type.
What is a RAG pipeline and do I need one?+
RAG (Retrieval-Augmented Generation) is how you let an LLM answer questions about your own data without baking that data into the model itself. If your use case involves 'ask questions about our documents / knowledge base / products,' you probably need RAG.
How do you evaluate AI output quality?+
We build evaluation frameworks before shipping — test sets of representative inputs with expected outputs, scored by a combination of automated checks and human review.
Can you add AI features to an existing product?+
Yes, this is the most common engagement. We integrate into your existing stack, adding AI as a feature layer rather than rebuilding from scratch.
What happens when an LLM produces bad output?+
We design for it. Validation, fallback logic, human-in-the-loop escalation where stakes are high. Production AI features need error handling just like any other code.
How much does running AI features cost in production?+
Highly variable. A simple summarization feature might cost pennies per thousand requests. An agentic pipeline with multiple LLM calls can cost dollars per run. We model costs before you commit and build cost monitoring in from day one.
/ who it's for
Common clients.
Have a project in mind?
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