Agents, RAG, MCP servers, chatbots, workflow automation, voice AI, AI search optimization. Real systems wired to real workflows. Built for SMBs that want AI doing actual work, not a slide deck demo.
Most agencies are still pitching AI strategy decks. We ship working systems. Here is the full stack we build, scoped to where your business actually needs the lift.
Agents that finish the job, not just chat.
Multi-step agents wired to your tools (CRM, email, Slack, Notion, calendars, ticketing). Built on Claude, GPT, or open models depending on the use case. Tool-calling discipline so the agent does work, not just guesses.
Your data, retrievable, accurate.
Retrieval-augmented generation over your docs, your knowledge base, your historical tickets, your contracts. Pinecone, Weaviate, Qdrant, or pgvector depending on volume. Citations and accuracy testing built in.
Standardized AI tool integration.
Custom MCP (Model Context Protocol) servers that let Claude, ChatGPT, Cursor, and other clients use your internal tools natively. The right way to wire AI into your stack going forward.
Zapier and Make work, scaled and owned.
n8n, Zapier, Make for the bread-and-butter workflows. Custom code (Python, TypeScript) for anything that hits scale or compliance limits. Wired to email, CRM, billing, support, the works.
Real customer support, not a deflection theater.
Web-embedded, Slack-native, or in-product chatbots that actually answer using your real product docs and historical support tickets. Escalation paths, accuracy guardrails, conversation analytics.
Inbound calls answered intelligently.
Voice agents on Vapi, Bland, Retell, or custom Twilio integrations. Inbound qualification, appointment booking, after-hours coverage. Routes to humans on the cases that need humans.
Get cited inside ChatGPT, AI Overviews, Perplexity.
Answer-first content, entity-rich structured data, FAQ schema everywhere, content engineered for the way LLMs extract answers. The new search surface most agencies are not even tracking.
Custom tools for internal teams.
Sales ops dashboards with AI summaries, marketing copy generators on your brand voice, ops triage tools, internal Q&A bots over your wiki. Real tools that save real hours per week.
AI-powered conversion assets.
Pricing calculators, product configurators, ROI estimators, lead-magnet quizzes. AI-augmented where it improves accuracy or recommendation quality. Designed for SEO and conversion as a primary surface.
Where to start, where to skip.
Audit of your stack, your data, your workflows. Prioritization by impact and feasibility. Roadmap of what to ship in 90 days, what to ship in 6 months, what to ignore. Most companies need fewer AI projects, better executed.
These are the use cases where Farin Innovations clients are already running AI in production. Real systems. Real ROI. Not slide decks.
Inbound emails and form fills routed, scored, and replied to in seconds. Sales gets only the leads worth a human.
Real product-doc-aware chatbots that resolve 30 to 60% of tier-1 tickets without escalation. Saves hours per week.
Internal AI tools that draft outlines, brief writers, source citations, and generate variants in your brand voice. Humans still ship the work, AI removes the blank page.
Auto-enrichment, deduplication, contact-stage updates, follow-up scheduling triggered by behavior. CRM that keeps itself current.
RAG over your wiki, runbooks, contracts, historical tickets. Slack-native or web-embedded. Onboarding compresses by weeks.
Content and schema engineered to be the source AI Overviews and ChatGPT cite for your category keywords. The new SEO.
A wrapper around ChatGPT branded as your product’s AI
A real RAG application over your data with citations and accuracy testing
A chatbot that hallucinates pricing and policy
A chatbot grounded in your docs, with escalation paths and guardrails
A 90-day "AI strategy" deliverable that ships nothing
Strategy paired with a working prototype inside the first 30 days
A no-code workflow that breaks every Tuesday
Production-grade automation written in code where reliability matters
A "we use AI" badge on your site that nothing else backs up
Real AI optimization on the content side and real AI tools on the ops side
A vendor lock-in to one model that doubles in price next year
Architecture that swaps Claude, GPT, Gemini, or open models per workload
Real RAG with proper retrieval, real agents with tool-calling discipline, real workflows in code where reliability matters. The opposite of "we use AI" badges.
Different models excel at different things. We architect so swapping is cheap, no vendor lock-in, no rewrite when pricing or quality shifts. The AI landscape is moving fast. We build for it.
Most "AI consulting" engagements deliver decks. We deliver the first running agent or automation inside the first month, then iterate from real usage signal.
Hallucination guardrails, citation discipline, accuracy testing, cost monitoring, fallback paths. The unsexy production-grade work most "AI builders" skip.
AI search citation engineering and AI tooling are the same competency: understand how the model picks, give it what it needs. We do both. Most agencies are starting one and ignoring the other.
The questions founders and operators ask before committing budget to AI. Real timelines, real costs, real architecture decisions.
Two related practices in one team. AI Optimization on the marketing side: getting your content cited by ChatGPT, Google AI Overviews, Perplexity, and the rest. AI Tooling on the ops side: agents, RAG, chatbots, workflow automation, voice AI, internal tools that save real hours per week. Most clients eventually want both.
First proof-of-concept inside 2 to 3 weeks of kickoff. First production system inside 30 to 45 days. From there it compounds. The 90-day roadmap typically delivers 2 to 4 systems in production. We do not run forever-strategy engagements.
Whatever wins per workload. Claude (Anthropic) for long-context reasoning and tool use. GPT (OpenAI) for general-purpose and broad ecosystem. Gemini for multimodal and Google integration. Open models (Llama, Mistral) where data residency or cost matters. We architect for swap-ability, not lock-in.
Retrieval-augmented generation: instead of just asking a model what it knows, you retrieve relevant snippets from your data first, then ask the model to answer using those snippets. This is the right approach when accuracy matters and the answer should reflect your specific docs, your ticket history, your contracts, your knowledge base. Most useful AI applications in business are RAG underneath.
Model Context Protocol: an open standard (created by Anthropic, adopted by everyone now) that lets AI models access tools and data sources in a standardized way. We build custom MCP servers that let Claude, ChatGPT, Cursor, and other AI clients use your internal tools natively. It is the right way to wire AI into your stack going forward.
It will if you build it wrong. We build with accuracy guardrails, citation discipline, retrieval grounding, and escalation paths. Production systems get accuracy testing and observability before they ship. Hallucination is an engineering problem, not an inherent AI problem.
You. Full source code, model API keys in your accounts, vector DB in your infrastructure, agents running on your hosting. If we ever part ways you keep everything we built. No proprietary AI platform tax, no licensing trap.
Custom by scope. A typical SMB AI engagement starts at $5K for an audit + roadmap and runs $8K to $25K per month for ongoing build and operate. Custom agents and RAG systems run $15K to $80K depending on complexity. AI search optimization runs alongside our content and SEO engagements. Pricing happens after a 30-minute scoping call.
Free AI audit. We map your current stack, the workflows eating hours, the use cases where AI is already a fit, and the three systems we would ship in the first 90 days. You see it on a 30-minute call. No deck, no pitch.
