Support triage, in-app assistants, churn signals, semantic search — AI embedded in your SaaS product and internal ops. $3,999/mo retainer, first feature live in 30 days.
- Analyze
- Automate
- Monitor
monthly retainer
Who this is for
SaaS founder or product lead who needs to ship AI features without an ML hire. Competitors are already moving. Support is stacking up. The question isn't whether to add AI automation for SaaS — it's which feature to ship first and how to do it without a six-month hiring cycle.
The pain today
- Competitors shipping AI features while your roadmap stays frozen
- Support tickets piling up with no capacity to triage them
- No ML expertise in-house and ML engineer interviews take months
- Previous AI experiments that ran great in demos but never reached production
- Uncertainty about data privacy, vendor lock-in, and API cost at scale
The outcome you get
- AI automations live in your SaaS product and ops on a $3,999/mo retainer
- First meaningful AI feature shipped within 30 days of kickoff
- Support triage that cuts response time 30 to 50 percent without replacing human agents
- In-app AI assistants tuned to your product's actual data and user workflows
- Full documentation and code ownership so your team can extend everything without me
Highest-ROI AI automations for SaaS right now
Three categories account for most of the real value I see SaaS companies capturing with AI automation. Support triage is first: an LLM reads each incoming ticket, suggests category, priority, and a draft response for human review. Response time drops 30 to 50 percent without removing the human from the loop. In-app assistants are second: contextual help, guided onboarding, natural-language data exploration. These lift feature adoption in a way that more documentation never does. Churn signals are third: the LLM reads usage patterns alongside qualitative signals (NPS scores, support sentiment, session drop-off) and flags at-risk accounts before they quietly churn. The specifics vary by product, but these three almost always deliver. I start with whichever fits your current bottleneck, not whichever is technically interesting.
AI automations live in your SaaS product and ops on a $3,999/mo retainer
AI features that live inside the product
Back-office automation is fine. Embedded AI inside the product itself moves the needle more. Five patterns show up across most SaaS verticals. Summarisation: long inputs or transcripts reduced to a digest on demand. Semantic search: users find things by meaning rather than exact keywords. In-app assistant: help, onboarding, and navigation via a chat surface trained on your own docs and product data. Content generation: first drafts of outputs your product already creates manually. Natural-language queries: users ask questions against their own data without writing SQL. Each is roughly two to four weeks of work. For SaaS competing with AI-native products, embedded AI is often the single differentiator that keeps you competitive without rebuilding the product from scratch.
30+: Active users.
My 30-day integration playbook
Week one: I audit current support tickets, user feedback, and product usage data to identify the highest-ROI AI opportunity. Week two: I build the first integration end-to-end. That means LLM provider setup, prompt engineering, output validation, and the user-facing surface. Week three: internal testing with your team. I tune based on real outputs, not synthetic test cases. Week four: production deploy with monitoring, cost tracking, and a rollback path in place. A single focused 30-day sprint consistently beats six-month AI transformation programs that generate decks but never ship. From month two onward I iterate on the first feature and add new ones based on actual usage data, not guesswork.
Build vs buy: when to use APIs, when to build custom
The honest answer: almost every SaaS company today should be building on top of existing LLM APIs, not training custom models. Pre-trained foundation models handle support triage, summarisation, semantic search, and content generation well enough for production use. The case for custom training is narrow: domain-specific language the foundation models genuinely do not understand, regulatory requirements that prohibit third-party APIs entirely, or inference volume so high that self-hosting is cheaper. Outside those three cases, the API route ships faster, costs less to maintain, and keeps you on the right side of the model improvement curve. I help you make that call in the first conversation, not after six weeks of scoping.
Pricing and what the retainer covers
One tier: $3,999/mo. The retainer covers AI integration work, prompt engineering, eval infrastructure, monitoring setup, and ongoing tuning. 14-day money-back guarantee. Cancel anytime. Your team retains 100 percent code ownership under Work Made for Hire. LLM API costs pass through at cost — typically $50 to $500/month for most SaaS products starting out, higher for heavy AI usage. Cost management is part of the monthly work: prompt compression, model routing (cheaper models for simpler tasks), response caching. The API accounts are yours. Cancelling the retainer does not affect your access to any of it.
What the Instill and Cuez work tells me about SaaS AI
Instill is a self-initiated product I built and run: an AI knowledge base where teams build and share skills via the MCP protocol. It has 30+ active users, 1,000+ skills saved, and powers 45+ projects. It is the clearest demonstration I have of what structured prompting at scale actually looks like in production — not a prototype but a live multi-user product. The Cuez engagement sits in the background of every AI performance conversation I have. I took a broadcast-SaaS API from 3 seconds to 300ms, 10x faster, alongside roughly 40 percent infrastructure cost reduction. Performance discipline and AI cost discipline come from the same place: knowing where the waste is and cutting it before it scales.
When to hire an ML engineer instead
My retainer is the right fit for practical LLM-based automation: prompt engineering, RAG architectures, workflow integration, eval infrastructure. That covers the majority of what SaaS companies need today. Hire a full-time ML engineer when you need custom model training on proprietary data, domain-specific fine-tuning at scale, or real-time ML serving where you are building the model itself as the product. For AI-native products where the core value proposition is a genuinely novel model, in-house ML capability from day one is the right call. For everyone else, an AI retainer moves the product forward without the six-month hiring overhead and the ongoing cost of a senior ML engineer salary. I tell you which bucket you fall into on the first call.
Recent proof
A comparable engagement, delivered and documented.
An AI knowledge base your whole team uses via MCP
A personal library for Skills, Agents, and Rules, built once, used across Claude, Cursor, and any MCP-compatible AI tool. Save your best workflows once. Run them anywhere.
Read the case studyFrequently asked questions
The questions prospects ask before they book.
My retainer is $3,999/mo. LLM API costs — OpenAI, Anthropic, or others — pass through at cost and typically run $50 to $500/month for a SaaS product at early AI usage volumes. Heavy users see $1,000 to $10,000/month in API costs. Cost management (prompt compression, model routing, caching) is part of the monthly work, so the total does not quietly compound. No setup fee, no per-feature billing.
Default setup uses enterprise tiers of OpenAI or Anthropic with data-processing agreements that explicitly prohibit using prompts for model training. For products handling PII, PHI, or financial data, I use no-data-retention API tiers. For workloads where even third-party APIs are off the table, self-hosted open-source models — Llama, Mistral — deployed on your own infrastructure are a viable path. The model choice is documented in your privacy policy and customer-facing DPA. I do not pick the setup that is easiest for me; I pick the one your compliance posture requires.
Anthropic Claude is my default for reasoning, long-context tasks, summarisation, and first drafts. OpenAI for embeddings and structured output. Both integrate cleanly and cost comparably for most SaaS workloads. For budget-sensitive or high-volume use cases, open-source models via Groq or Replicate can cut API costs significantly with some quality trade-off. I pick based on the task, the sensitivity of the data, and the cost target — not brand preference. You own all the API accounts regardless of which provider I recommend.
I start with golden-set evals: 20 to 50 hand-crafted test cases with known-good outputs for each AI feature. Every prompt change runs against that set to catch regressions before they reach users. In production, lightweight sampling monitors output quality on an ongoing basis. For high-stakes outputs — customer-facing responses, financial recommendations — a human-in-the-loop step captures feedback that feeds back into prompt tuning. For SaaS products running multiple AI features, a proper eval framework (LangSmith, Braintrust) is worth the setup cost.
Yes, if the integration is architected correctly from the start. I build an abstraction layer between your application logic and the LLM provider so that swapping models is a configuration change rather than a code rewrite. That also means you can run two models in parallel for A/B testing or cost comparison. Provider portability is something I design in from day one, not an afterthought.
It covers LLM-based automations: support triage, in-app assistants, semantic search, content generation, churn signal detection, RAG over your product's data. It does not cover custom model training, computer vision, real-time speech processing, or fine-tuning foundation models on proprietary datasets — those require dedicated ML engineering. For most SaaS products, LLM-based automation covers 80 percent or more of the AI use cases on the roadmap without needing any of that infrastructure.