AI that actually works in your product.
Not a ChatGPT wrapper. Real AI engineering — RAG pipelines trained on your data, autonomous agents that complete tasks, and LLM integrations that solve your users' specific problems. Shipped in 3–6 weeks.
Starting From
Everything delivered, nothing missing.
LLM Integration & Prompt Engineering
OpenAI GPT-4o, Anthropic Claude, Google Gemini — wired into your product with structured outputs, function calling, and cost-optimized prompts.
RAG Pipeline (Retrieval-Augmented Generation)
Your documents, knowledge base, or database turned into a searchable vector store — so AI answers with your data, not hallucinations.
Vector Database Setup
Pinecone, Weaviate, or pgvector — set up with chunking strategies, embedding pipelines, and similarity search tuned for your content.
AI Agent Workflows
Autonomous agents using LangChain, LlamaIndex, or custom architectures — that browse, research, write, and execute multi-step tasks.
Fine-Tuning & Custom Models
Fine-tune OpenAI or open-source models on your domain data for higher accuracy, lower latency, and reduced cost per call.
Streaming UI
Real-time token streaming so users see AI responses as they generate — for the ChatGPT-like experience your users expect.
AI Cost Optimization
Caching, model routing, prompt compression, and batching — to keep your OpenAI bill under control as you scale.
Evaluation & Testing Framework
AI response quality monitoring, regression tests, hallucination detection, and a/b prompt testing — so your AI improves over time.
AI Safety & Guardrails
Content moderation, PII detection, output filtering, and rate limiting — to keep your AI product safe for production use.
How we take you from zero to launch.
AI Use Case Discovery
We map where AI creates the most value in your product — not where it's technically impressive but where it solves a real user problem.
Data Audit & Model Selection
We review your existing data, choose the right model (GPT-4o, Claude 3.5, Llama 3, etc.), and decide on RAG vs fine-tuning vs base prompting.
Proof of Concept (Week 1)
A working prototype of the AI feature — running against your real data, with real latency and real output quality you can evaluate.
Pipeline Build & Integration
Full pipeline development: ingestion, embedding, retrieval, generation, and output formatting. Wired into your existing product.
Evaluation, Red-Teaming & Tuning
Systematic testing for accuracy, hallucinations, edge cases, and adversarial inputs — iterating until quality meets your standard.
Production Deployment & Monitoring
Deployed with observability tools (Langfuse, Helicone, or custom) so you can see every LLM call, cost, and quality metric in real time.
Who builds this with us?
Founders, enterprises, and product teams across every vertical — here's what they build.
AI Contract Review & Risk Analysis
Upload any contract, get clause-by-clause risk summaries, missing terms flagged, and redline suggestions — trained on 10,000+ legal agreements.
AI Support Agent with Ticket Escalation
Resolves 60% of support tickets automatically using your documentation and past ticket history — escalates to humans only when needed.
AI Shopping Assistant & Recommendation Engine
Conversational product discovery — understands natural language intent ('something for a beach vacation under $100') and returns contextually ranked results.
AI Financial Report Summarizer
Upload 200-page annual reports and get structured executive summaries, KPI extraction, and trend analysis — in seconds.
Clinical Note & Coding Assistant
AI that listens to patient-doctor conversations, generates SOAP notes, and suggests ICD-10 codes — reducing documentation time by 70%.
AI Code Review & Debugging Agent
PR-level code review with security, performance, and style feedback — plus an AI debugger that explains errors and suggests fixes in plain English.
Battle-tested technologies we use.
LLM Providers
AI Frameworks
Vector & Storage
Observability
Why clients choose us — and stick around.
We build AI products, not demos
Anyone can wire up an OpenAI call. We build AI features that survive real users, edge cases, cost constraints, and production load.
Deep prompt engineering expertise
We've run thousands of prompt experiments across dozens of products. We know what works, what fails, and why.
RAG that actually retrieves correctly
Most RAG pipelines fail silently — wrong chunks, poor embeddings, bad retrieval. We've solved these problems at scale.
Model-agnostic architecture
We build against abstraction layers so you're not locked to a single LLM provider — swap models as prices and capabilities change.
Common questions, honest answers.
What's the difference between RAG and fine-tuning?
RAG retrieves relevant information at query time from your documents — better for frequently changing content. Fine-tuning bakes knowledge into the model weights — better for consistent tone, format, or domain-specific style. Often the best solution uses both.
How do you handle hallucinations?
Through grounding (citations, source references), output validation, evaluation frameworks, and guardrail layers. We also set honest expectations with your users — AI confidence indicators help.
How much does OpenAI cost to run at scale?
It varies hugely. We'll estimate cost per user per month during scoping based on your use case, then optimize aggressively with caching, model routing, and prompt compression to minimize it.
Can you use open-source models instead of OpenAI?
Yes. Llama 3, Mistral, Qwen, and others can run on your own infrastructure for full data privacy and zero per-token costs. We'll recommend the right trade-off for your use case.
What if the AI quality isn't good enough after launch?
We include an evaluation framework so quality is measurable. If initial accuracy is below target, we iterate on prompts, retrieval, and data quality — within the agreed scope.
Ready to build?
Tell us what you need. We'll scope it, price it, and start within 48 hours.