Add intelligent features to your product - from chatbots and content generation to data analysis pipelines and smart automation.
AI Integration is the engagement we run when an AI feature needs to ship to real users, not just demo well. AI-authored code carries 1.7× more issues than human-authored code (CodeRabbit, Dec 2025). Most "AI features" in the wild ship without the eval harness, prompt boundary, or cost controls a real production system needs. We build them in from day one.
Every project is different, but here's what each tier typically looks like. Book a free assessment and we'll scope yours precisely.
A single AI feature integrated into your existing product - a chatbot, content generator, or smart search. Production-ready, not a demo.
AI grounded in your data using RAG pipelines. Your customers get answers they trust because the AI actually knows your product, docs, and knowledge base.
MCP servers, multi-model orchestration, workflow automation - scoped to your systems. Let's design it together.
A production-ready RAG pipeline or chatbot integration usually ships in 3-5 weeks. MCP servers and agent orchestration can run 4-6 weeks depending on the number of internal systems we connect to. We deliver a working evaluation harness in week one so you can measure quality before scaling.
Fixed-price between $20,000 and $35,000 for a typical engagement. That covers retrieval design, model selection, eval framework, observability, and the production deploy. Ongoing tuning and model-cost optimization roll into a monthly retainer if you want it.
Model routing, prompt caching, and retrieval-first architecture. We typically cut AI inference cost 60-80% versus a naive GPT-4 wrapper by routing easy queries to smaller models and caching deterministic responses. You see token spend on a per-feature dashboard from day one.
We build retrieval-grounded systems with explicit guardrails: source citations, refusal policies, PII redaction at the prompt boundary, and an evaluation suite that runs on every deploy. Hallucination rate and groundedness are tracked metrics, not afterthoughts.
Whichever fits the workload. We benchmark Claude, GPT-4, Gemini, and open-weight models like Llama and Qwen against your actual data. Most production systems we ship use two or three models routed by task. You are not locked into a single vendor.
The honest comparison most agencies won't put in writing. Same dimensions, three real options, no marketing math.
| Dimension | Bytewise integration | DIY with OpenAI SDK | Off-the-shelf chatbot |
|---|---|---|---|
| Time to production | 3–5 weeks with eval harness | 2–4 months with rewrites | Hours, but quality varies |
| RAG quality | Tuned retrieval + grounding metrics | Default chunking, hit-or-miss | Pre-baked, often hallucinates |
| Cost optimization | Model routing cuts 60–80% spend | Easy to leave money on the table | Vendor margin baked in |
| Vendor lock-in | Multi-model, swappable | Tied to one provider SDK | Tied to chatbot vendor |
| Ongoing tuning | Monitored, retrained, retainerable | On you to maintain | Static unless vendor updates it |
