AI Integration
AI features that ship to production.
Starting at
From $5,000
Timeline
2–6 weeks
Every founder has been pitched AI. Most of what's been built is a thin ChatGPT wrapper that answers wrong, hallucinates citations, and falls over the moment you upload a real document. We build the kind that doesn't.
We add AI to existing products and build AI-first products from scratch. RAG knowledge bases with proper citation, document processing pipelines for PDFs and DOCX, voice transcription with Deepgram or Whisper, content generation with Claude, and workflow automation that actually replaces a human task.
What you don't get: generic GPT-4 wrapper, demo that breaks on real data, vague "AI strategy" decks. What you do get: working code, evaluation against real inputs, cost estimates per query, and a system that's safe to put in front of paying customers.
What's included
Every project ships with everything below — no surprise add-ons.
RAG knowledge bases
Upload docs, ask questions, get answers with exact source citations. Vector embeddings (OpenAI, Voyage), Supabase pgvector or Pinecone, Claude or GPT-4 for generation. Same architecture that powers Knoah.
AI chatbots & agents
Conversational interfaces grounded in your data. Tool use, function calling, multi-turn memory, and guardrails so it doesn't go off-script.
Document processing
PDF, DOCX, image OCR, structured data extraction. Process invoices, resumes, contracts, medical records — at scale, with Claude or GPT-4 Vision.
Voice transcription & analysis
Deepgram (real-time, fast, cheap) or Whisper (offline, accurate). Speaker diarization, timestamps, and downstream Claude analysis (summaries, action items, sentiment).
AI content generation
Resume rewrites, blog post drafts, product descriptions, email replies. With your tone, your data, and your guardrails. Same engine that powers ResumeIdol.
Video AI pipelines
Auto-clip generation, captions, viral moment detection. Deepgram + Claude + FFmpeg. Same pipeline that powers Clippified.
Workflow automation
n8n, Zapier, or custom Node workers. Email triage, invoice generation, lead scoring, report writing — anything where a human is currently doing pattern-matching.
How it works
Same playbook for every project. Predictable, weekly demos.
Week 1 — Spec + evaluation harness
What does "correct" mean for your use case? We define eval criteria up front and build a test set. This is the step most agencies skip and most AI products fail on.
Week 2–4 — Build + tune
Pipeline implementation, prompt engineering, model selection (Claude Opus vs Sonnet vs Haiku, GPT-4 vs 4o, when to fine-tune). Run against eval set every iteration.
Week 5 — Production hardening
Rate limiting, cost monitoring per query, caching, fallback models, error handling. So a 5x traffic spike doesn't blow your API bill or crash the product.
Week 6 — Launch + handover
Deploy, monitor in production, hand over the eval harness so you can keep improving after we're gone.
Live products built this way
Click any of these — they're running in production right now.
Knoah
RAG knowledge base. Sub-2-second response on 10K+ document corpora, exact citations, Claude under the hood.
Clippified
Video → viral clips. Deepgram transcribes, Claude picks moments, FFmpeg renders with captions. Sub-90-second processing.
ResumeIdol
AI resume tailor. Claude rewrites bullets to match a job description while preserving voice. Sub-10-second generation.
Frequently asked
The questions we get most often before someone signs.
Will it hallucinate?
Less than out-of-the-box GPT, because we ground responses in your data via RAG and force exact citations. We can't promise 0% — nobody can — but we can promise an eval harness so you can measure it.
Claude or GPT?
Depends on the task. Claude is better at long-context reasoning, careful instruction-following, and writing. GPT-4o is better at multimodal and faster. We pick per use case and often use both. We're also vendor-agnostic — if you'd rather use only one provider, we'll respect that.
What about cost at scale?
We cost-model up front. Typical RAG chatbot is $0.01–$0.10 per query depending on context size and model. We add caching, prompt compression, and routing to cheaper models where possible. You'll know the per-query cost before launch.
Can you fine-tune?
Sometimes. 90% of the time, prompt engineering + RAG beats fine-tuning. The other 10% (very specific style, structured output reliability) we'll fine-tune Haiku, Llama, or a small open-source model — usually for $1K–3K extra.
Is my data safe?
We default to Claude (Anthropic) and OpenAI's enterprise APIs which don't train on your data. For regulated workloads (HIPAA, etc.) we can route to AWS Bedrock or Azure OpenAI inside your own cloud account.
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