Build an AI-powered app — LLM integration done right, not as a gimmick.
AI features are valuable when they solve a real user problem. Document summarization, intelligent search, content generation, data extraction, and conversational interfaces — AI that reduces real work is worth building.
Founder or product team who needs to integrate LLM capabilities into a product — and wants it built by someone who knows when AI is the right tool and how to build it reliably
Most "AI features" in products fall into one of two failure modes: AI theater (looks like AI, doesn't do anything useful) or AI brittleness (works in demos, breaks in production).
When AI features are worth building:
Document processing: Legal contracts, medical records, financial statements — documents humans read and extract information from. LLMs are excellent at this at scale. Extract structured data from PDFs, summarize documents, answer questions about document content.
Semantic search: Keyword search finds exact matches. Semantic search finds conceptually related content. RAG (Retrieval-Augmented Generation) with vector embeddings lets users ask natural language questions across a document library.
Content generation: First drafts, structured content from data, personalized emails from templates. AI that reduces writing time by 70% is valuable. AI that replaces human judgment is not yet reliable.
Conversational interfaces: Chatbots that answer questions about a specific knowledge base. Customer support deflection for common questions. Onboarding assistants that guide users through setup.
LLM production architecture: Model selection (GPT-4o vs. Claude vs. Gemini based on use case). Prompt engineering and versioning. Context window management. Streaming responses for perceived performance. Cost controls (token limits, caching, model routing). Error handling for model failures.
AI-powered application deployed — LLM integration with proper prompt engineering, cost controls, error handling, and the UX that makes AI features actually usable
LLM integration
OpenAI, Anthropic, or Google Gemini API integration
RAG pipeline
document ingestion, embedding, vector search, and generation
Prompt engineering
structured prompts with few-shot examples and output validation
Streaming UI
streaming LLM output with real-time user feedback
Cost controls
token budget management and model routing by task complexity
AI quality monitoring
logging, evaluation, and prompt improvement workflow
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
AI-powered application deployed — LLM integration with proper prompt engineering, cost controls, error handling, and the UX that makes AI features actually usable
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
AI features have defined scope: data ingestion, model integration, UI, and cost controls. Fixed-price from the spec.
Questions, answered.
GPT-4o for complex reasoning tasks. Claude 3.5 Sonnet for long context and document processing. Gemini Flash for high-volume, cost-sensitive tasks. Model selection made per-use-case in the spec — many applications use multiple models.
RAG (grounding responses in retrieved documents) reduces hallucination significantly. Output validation for structured data extraction. Human-in-the-loop for high-stakes decisions. Confidence scoring and fallback to "I don't know" for out-of-scope questions.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.