AI Application Development
RAG pipelines, agents, and chatbots built for production traffic, not just the demo.
Who this is for
If any of this sounds like you, we should talk.
You have a Colab notebook that works and a product that does not
The model is right. The pipeline is fragile. You need someone to wrap it in a real product with auth, rate limiting, caching, and monitoring.
You want to add AI to a product you already have
Smart search, document Q&A, content generation, summarization, agents. You need it built into the existing product cleanly, not bolted on as a side panel.
You are building an AI-first SaaS
Chatbots, AI assistants, copilots, RAG-powered tools. You want the model layer, the data layer, and the product layer built by people who understand all three.
Your LLM bill is growing faster than your revenue
Token spend is out of control. Latency is bad. You need caching, prompt design, and model routing done by engineers who actually ship AI in production.
What changes for you
Outcomes you can point to, not features you can ignore.
- An AI product that holds up at 10x your current usage without falling over.
- Token spend down through caching, prompt engineering, and smart model routing.
- Real evaluation pipelines so model and prompt changes do not silently break the product.
- A clean separation between the model layer and the product layer so you can swap models without rewriting the app.
What is included
Scope, organized by phase.
Discovery (Phase 1 of 5)
Phase 1
Discovery
What we lock down in this phase before moving on.
- Use case audit and what AI is actually right for here
- Model selection (OpenAI, Anthropic, open-source, hybrid)
- Data inventory for RAG, fine-tuning, or context engineering
- Latency, cost, and accuracy targets written down
How an engagement works
From hello to handoff, step by step.
You book an AI scoping call
We talk use case first, model second. Most AI projects fail because the use case was wrong, not because the model was wrong.
You get a scoped plan within 48 hours
Architecture, model choice, eval plan, cost model, and timeline. The unknowns get flagged, not hidden.
We build with evals from sprint one
Eval harness before the feature. Every prompt change runs against the harness. No silent regressions.
We deploy with feature flags and observability
AI features roll out to 1 percent, then 10, then 100. Every completion is observable. Every cost spike is alertable.
We iterate on prompts, models, and data
Post-launch, the work is tuning. Better prompts, cheaper models, smarter retrieval. The product gets sharper over time, not staler.
Case study
WitsCode AI Search content engine
A library of AI search content that drives our own pipeline and proves the playbook we sell. The credibility moat is the case study.
AI search was eating organic traffic for every SaaS founder we worked with. We needed to prove we could not just talk about AI optimization, but build the systems that put a brand in front of LLMs.
We built our own AI search content engine, generating, optimizing, and tracking content across LLM citation surfaces. RAG over our own knowledge base, eval pipelines for content quality, and a citation tracking layer to measure presence in ChatGPT, Perplexity, and Gemini answers.
Why us
What you get with WitsCode that you don't get elsewhere.
We build AI in production, not in notebooks
Most AI work fails between the demo and the user. We focus on the infrastructure (caching, evals, observability, guardrails) that makes a demo into a product.
Model-agnostic by design
We do not marry you to one provider. The product layer talks to an abstraction. Swapping OpenAI for Anthropic or for an open-source model is a config change, not a rewrite.
We use AI in our own work, openly
We ship our own AI products. We write the playbooks we sell. When you hire us, you get the same engineers who use these tools every day, not a team learning on your bill.
WitsCode rebuilt our Shopify store so it finally converts the traffic we were already getting. They understand speed and storytelling in equal measure, and the store has been a real growth lever since launch.
Frequently asked
Questions before you reach out.
OpenAI (GPT-4, GPT-4o, o1), Anthropic (Claude family), Google (Gemini), and open-source models (Llama, Mistral, Qwen) via providers like Together, Groq, or self-hosted. We pick based on cost, latency, and accuracy targets, not on which vendor we like.
Yes, this is most of our AI work. We integrate cleanly into your existing codebase, respect your data model, and ship behind feature flags so you can roll it out carefully.
We use providers with clear data retention policies (OpenAI, Anthropic, and Google all offer zero-retention options on their enterprise tiers). For sensitive data we route to self-hosted open-source models. PII redaction at the prompt layer is standard.
Project scope determines cost, not a price list. We give you a firm number at scoping. Token spend is a separate ongoing line item that we help you forecast and control.
Eval harness from sprint one. Golden test sets, automated scoring (LLM-as-judge plus rule-based), and CI integration so a prompt or model change fails the build if it tanks quality.
Yes. Chunking, embeddings, vector storage, retrieval, reranking, and citation, set up around your data. We work with pgvector, Pinecone, Qdrant, and Weaviate.
Yes, standard mutual NDA before any technical conversation.
Ready to ship AI that holds up in production?
Book an AI scoping call and get a fixed plan within 48 hours.