Integrating AI capabilities into an existing product or workflow in the US in 2026 typically costs $10,000 to $120,000. Most mid-market operators adding LLM features to a live product land at $25K-$65K.
The floor is a single-feature prompt pipeline wired to an existing app. The ceiling is a multi-modal AI layer with custom fine-tuning, retrieval-augmented generation (RAG), vector databases, and observability infrastructure. Most production AI integrations land at $30K-$75K once evaluation, guardrails, and latency optimization are in scope. The single biggest predictor of where a specific engagement lands is scope discipline, operators who lock the spec in the first two weeks save 20-40% of total project cost over the next three months. Operators who let scope expand mid-build pay the inverse penalty. Either way, the $10K to $120K range is descriptive, not prescriptive: it reflects what a competent US vendor charges in 2026 for the work as scoped, not what a finished engagement has to cost.
| Component | Low | High |
|---|---|---|
Discovery + model selection + architecture | $3K | $18K |
Prompt engineering + evaluation harness | $3K | $20K |
RAG pipeline (chunking, embeddings, vector store) Only if document/knowledge retrieval is in scope. | $4K | $30K |
API integration (OpenAI, Anthropic, or open-source) | $2K | $12K |
Fine-tuning or RLHF (if required) Most integrations don't need fine-tuning; base models + good prompting suffice. | $0 | $25K |
Observability + token cost monitoring + guardrails | $2K | $10K |
UI/UX for AI-facing surfaces | $2K | $15K |
Discovery + model selection + architecture
Prompt engineering + evaluation harness
RAG pipeline (chunking, embeddings, vector store)
Only if document/knowledge retrieval is in scope.
API integration (OpenAI, Anthropic, or open-source)
Fine-tuning or RLHF (if required)
Most integrations don't need fine-tuning; base models + good prompting suffice.
Observability + token cost monitoring + guardrails
UI/UX for AI-facing surfaces
Hosted APIs (OpenAI, Anthropic) are faster to integrate but carry per-token cost at scale. Self-hosted open-source models (Llama, Mistral) have higher up-front engineering cost but lower marginal cost at volume.
Simple RAG on 100 documents is a different cost class than multi-source RAG across millions of records with hybrid search and reranking. The architecture decision made in week one determines most of the build cost.
Production AI features without evaluation are a liability. Building an eval harness adds 20-30% to project cost but prevents the invisible quality regression that kills user trust.
Real-time AI features (< 500ms) require different architecture than async features. Streaming, caching, and batching add real engineering cost when latency SLAs are strict.
Healthcare, finance, and legal verticals add substantial cost for data residency, audit logging, and PII handling. A HIPAA-aligned AI integration costs 25-40% more than a general-purpose one.
Inparlor AI integration projects start at $18,000. Most production integrations land at $35K-$80K. We require a paid discovery sprint for AI work because the architecture decision is too consequential to scope by email. The premium over the floor of the market reflects scope we don't itemize, measurement infrastructure, post-launch stability, and a documented handoff that survives whoever happens to be on our team six months from now. Our proposals are itemized line-by-line so you can see what you're paying for; we'd rather lose the deal on transparent pricing than win it by hiding the math.
Custom quote
itemized proposal within 48 hours
Chatbots, AI agents, and RAG assistants that ship to production, not demos.
Full AI Chatbots & Agents breakdownNo-code AI wrappers (Zapier AI, Make.com with OpenAI steps) for $500-$5K. Works for internal automation. Won't work for customer-facing features where latency, quality, and reliability are observable. The honest framing: cheaper vendors exist at every tier, Fiverr at the bottom, offshore agencies in the middle, established US-based mid-market shops at the top. The cost-quality curve is real but rarely linear. Going from a $5K vendor to a $15K vendor usually produces a meaningfully different outcome; going from $15K to $45K often produces a refinement, not a transformation. Where you sit on that curve depends on the cost of being wrong, not the budget you have available.
(Build cost) ÷ (hours automated per year × loaded hourly cost) + (feature-driven revenue uplift)
$45K AI integration automating 3 hours/day of a $90K analyst's work = $33K/yr labor savings. Plus a new AI-powered feature driving 15% better conversion in a key funnel = $120K/yr incremental revenue at 40% margin. Payback in under 6 months on the combined math.
We'll send back an itemized proposal, scope, line items, timeline, and the team that would actually run the engagement. No discovery call to schedule a discovery call.
See the AI Chatbots & Agents serviceChatbots, AI agents, and RAG assistants that ship to production, not demos. Scoped and quoted individually — itemized proposal within 48 hours.