We build AI into
your product — not
as a demo, as a feature.
OpenAI, Anthropic, Gemini. RAG pipelines, LLM chat, computer vision. We've shipped AI into real products — an AI shopping assistant, an AI content platform, a document analysis system. Not prototypes. Things that work in production.
Fixed-price MVPs from $35k · No hourly billing surprises
What we build
We don't add an AI chatbot to your homepage and call it done. We wire AI into the workflows that actually matter in your product.
LLM-powered features
Chat interfaces, smart search, summarization, classification, extraction — wired into your product using OpenAI, Anthropic Claude, or Gemini. We handle prompting, streaming, rate limiting, and fallbacks.
RAG pipelines
Retrieval-Augmented Generation — your documents, knowledgebase, or database as the AI's context. Vector embeddings, semantic search, re-ranking. Pinecone, pgvector, Weaviate — we pick what fits.
AI-native product builds
Building a product where AI is the core feature, not a bolt-on. We've done this with Mebag (AI shopping) and Tully AI (AI content). Full-stack — from the model layer to the UI.
AI automation workflows
Take the repetitive work out — document parsing, data classification, email triage, report generation. We build the pipeline, hook it into your existing system, and wire up the monitoring so you know when it breaks.
Document & data intelligence
Upload a PDF, get structured data back. Parse contracts, invoices, medical notes, research papers. OCR plus LLM extraction — handles the messy stuff that regex-based parsers give up on.
AI integration into existing SaaS
Already have a product? We can add AI-powered features without rebuilding from scratch — smart search, copilot suggestions, automated summaries. Works with your current stack and data model.
Who this is for
Good fit
- SaaS founders adding AI features to an existing product
- Ops teams with repetitive document/data workflows
- Founders building a product where AI is the core value prop
- Companies with proprietary data they want to query conversationally
- Products needing smart search, summarization, or classification at scale
Probably not you
- You want a ChatGPT wrapper with your logo — just use the API directly
- You need basic data processing that doesn't require an LLM
- Budget under $15k — AI features done right aren't cheap
- You need the model itself trained from scratch (we integrate, we don't train foundational models)
How we approach AI projects
AI features that work in production are 20% model and 80% everything around it.
Use-case scoping
We push back early on vague "add AI" asks and pin down: what problem, what workflow, what success looks like, and whether AI is actually the right tool. Sometimes it isn't.
Spike & prototype
Before we build anything production-grade, we run a fast spike: real data, real prompts, real outputs. We know if the approach works before you spend $30k finding out it doesn't.
Pipeline architecture
Data ingestion, chunking strategy, embedding model, retrieval logic, prompt design, caching layer, streaming — we architect the full pipeline so it's maintainable after we hand it over.
Build & integrate
We build the AI features and integrate them into your product or existing codebase. Streaming, error handling, rate limit management, fallback behaviour — all production-ready.
Eval & monitoring
We set up evals so you can track output quality over time, and observability so you know when the model starts drifting or the retrieval is degrading. AI features need ongoing attention — we build that in.
Tech we use
LLM APIs
Frameworks
Vector & storage
Backend
Pricing
AI development cost depends on the complexity of the pipeline and how deep the integration runs.
One AI-powered feature integrated into your existing product — chat interface, smart search, document extraction, or summarization. Includes prompting, streaming, and basic evals.
- 1–2 LLM-powered features
- API integration + streaming
- Basic eval setup
- 6–10 week delivery
Full RAG pipeline on your data + multiple AI features built into the product. Vector database, retrieval tuning, prompt engineering, UI, monitoring.
- Full RAG pipeline
- Vector store + embeddings
- Multiple LLM features
- Observability + evals
- 12–20 week delivery
A full product where AI is the core feature — from architecture to launch. Multi-tenant, multi-user, production-grade. The Mebag and Tully AI tier.
- Full product build
- Multi-model orchestration
- Custom fine-tuning (if needed)
- Full SaaS infrastructure
- 20–36 week delivery
Scoping calls are free. We'll tell you honestly what tier fits your project and why.
AI we've shipped
Not proof-of-concepts. Things in production that users actually use.
Mebag — AI-powered shopping assistant
Mebag's core feature is an AI shopping assistant that understands what the user actually wants — not just keyword matching. Natural language product discovery, personalised recommendations, cart suggestions. Built on GPT-4o with custom retrieval on the product catalogue. Response time under 800ms in production.
Tully AI — AI content generation platform
Tully AI is a full content platform built around LLM-generated drafts — blog posts, social content, ad copy. Multi-model: uses Claude for long-form, GPT-4o for structured outputs. Custom prompt templates, brand voice training, export pipeline. Built from scratch — not a thin wrapper.
COMPASS — clinical data extraction & analysis
Ball State University's COMPASS platform for autism research needed to process clinical notes, session transcripts, and assessment documents — and extract structured data for research analysis. LLM-based extraction with structured output schemas. Zero hallucination tolerance — we built verification layers.
Common questions
Do you train custom models or just use the APIs?
We use the APIs — OpenAI, Anthropic, Google. Full model training from scratch is a research-level undertaking that costs millions. For almost every business use case, fine-tuning or RAG on top of an existing model is the right answer. We'll tell you if your case is actually an exception.
How do you handle data privacy with third-party LLM APIs?
We architect this carefully. Sensitive data gets filtered or anonymized before it hits the API. We use Azure OpenAI or AWS Bedrock when data residency matters. Anthropic, OpenAI, and Google all offer enterprise agreements with data processing terms — we've navigated this with healthcare and legal clients before.
What happens when the AI gives wrong answers?
That's the eval problem — and it's what most agencies skip. We set up evaluation pipelines from day one: golden datasets, output quality metrics, regression testing. We also build in UI-level signals so users can flag bad outputs. It's not "ship and hope" — it's an ongoing quality process.
How long does an AI feature take to build?
A single well-scoped AI feature: 4–8 weeks. A full RAG pipeline: 10–16 weeks. An AI-native product: 5–9 months. The spike phase alone takes 1–2 weeks — that's the phase where we prove the approach works before committing to the full build.
Can you add AI to our existing system?
Yes, that's one of the most common engagements. We've integrated AI features into existing SaaS products in Angular, React, Node, and .NET stacks. We audit your data model and API surface first, then design the integration so it doesn't require a full rewrite.
What's your minimum budget for AI work?
$12k for a well-scoped single feature. Below that, the architecture, evaluation, and production hardening you actually need gets skipped — and you end up with a demo that breaks in week three. We'll tell you upfront if a project is under-budgeted.
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Got something to build?
Tell us what it is.
30 minutes. No slides. We'll listen, ask the right questions, and tell you honestly if we can help — or why we can't. That's it.
Free 30-min scoping call
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