Add AI to your product — without the hype.
We integrate AI capabilities into existing products and workflows. LLMs, vector search, RAG pipelines — practical implementations that solve real problems, not demos that impress investors.
AI features that users actually use.
We focus on AI integrations that deliver measurable value — not chatbots bolted onto the side of your product. Here are the patterns we implement most often.
Semantic search
Replace keyword search with vector embeddings that understand meaning. Users find what they're looking for even when they don't know the exact terms.
RAG pipelines
Retrieval-Augmented Generation that grounds LLM responses in your data. Answer questions about your docs, knowledge base, or product data with citations.
Document processing
Extract structured data from PDFs, invoices, contracts, and forms. Classify, summarize, and route documents automatically.
Content generation
Product descriptions, email drafts, report summaries — AI-generated content with human review workflows and brand voice consistency.
Classification & tagging
Automatically categorize support tickets, tag content, detect sentiment, and route items to the right team or workflow.
AI-powered workflows
Multi-step AI agents that handle complex tasks — research, analysis, data transformation — with human oversight at decision points.
Engineering-first AI. Not magic, not hype.
AI is a tool, not a strategy. We treat it like any other engineering problem — define the requirement, choose the right approach, measure the results, and iterate.
Start with the problem, not the model
We don't start by picking an LLM. We start by understanding what your users need, what data you have, and whether AI is actually the right solution. Sometimes a good search index beats a vector database.
Prototype fast, validate early
We build working prototypes in days, not weeks. You see real results on your real data before we commit to a full implementation. If the approach doesn't work, we pivot early — not after months of development.
Cost-aware architecture
LLM API calls add up fast. We design for cost efficiency — caching, model selection (use GPT-4o-mini where Claude Haiku would do), batch processing, and fallback strategies that keep your AI bill predictable.
Evaluation-driven development
Every AI feature gets an evaluation framework. We define what 'good' looks like, measure against it systematically, and use evals to guide prompt engineering and model selection — not vibes.
AI integrations that deliver.
Real results from AI features we've shipped to production.
Reduction in manual document processing time.
Improvement in search relevance over keyword search.
Average response time for RAG queries.
The tools behind our AI integrations.
We work with the leading AI providers and open-source tools — choosing the right combination for each use case.
OpenAI, Anthropic Claude, open-source models via Ollama
pgvector, Pinecone, Qdrant
Vercel AI SDK, LangChain, LlamaIndex
Vercel, AWS Bedrock, modal.com for GPU workloads
The companies winning with AI aren't building their own models. They're integrating existing ones into workflows that matter.
Kenny T.
Founder & Engineer at Ketryon
AI Integration FAQ
Common questions about adding AI to your product.
Not necessarily. LLMs and embedding models work well even without training data — you can use them out of the box for text generation, classification, and search. RAG pipelines work with whatever documentation or knowledge base you already have. You don't need a data science team to get started.
It depends on the model and volume. A typical RAG query using Claude Haiku costs under $0.001. We design for cost efficiency from the start — caching, model tiering, and batch processing keep costs predictable. We'll provide cost estimates during our initial assessment.
Yes, with the right architecture. We use API providers with enterprise data policies (no training on your data), or deploy open-source models on your own infrastructure for full data sovereignty. GDPR compliance is built into our approach.
Every AI system has failure modes. We mitigate this with retrieval grounding (RAG), confidence thresholds, human review workflows, and clear UX that communicates uncertainty. We also build evaluation frameworks to measure accuracy systematically, not anecdotally.
That's our specialty. Most of our AI work is integrating capabilities into existing applications — not building AI products from scratch. We assess your current architecture, identify the highest-value integration points, and implement incrementally.
Ready to add AI to your product?
Tell us what you're trying to automate or improve. We'll give you an honest assessment of whether AI is the right approach — and if so, how to implement it.