Add AI to your SaaS product without hiring an ML team
Support triage, in-app assistants, churn signals, content summarisation — AI integrated into your SaaS product and internal ops. $3,000/mo retainer.
Who this is for
SaaS founder or product lead wanting to add AI features without hiring an ML team, often with competitors already shipping AI and support tickets piling up.
The pain today
- Competitors are shipping AI features and the pressure is on
- Support tickets piling up without enough team to triage
- No ML expertise in-house and hiring ML engineers takes 6+ months
- Previous AI experiments were flashy demos that did not ship to production
- Data privacy and model choice feel overwhelming
The outcome you get
- AI automations shipped to SaaS product and ops on $3,000/mo retainer
- First meaningful AI feature live within 30 days
- Support triage that reduces response time 30 to 50 percent
- In-app AI assistants tuned to your product
- Clear documentation so your team can extend without me
Highest-ROI AI automations for SaaS in 2026
Three categories deliver real ROI fast. Support triage — LLM reads incoming ticket, suggests category, priority, and initial response for human review. Cuts response time 30 to 50 percent without replacing human agents. In-app assistants — contextual help, onboarding, data exploration. Lifts feature adoption without adding documentation. Churn signals — LLM reads usage patterns and qualitative data (NPS, support sentiment) to flag at-risk accounts before they cancel. The specifics vary per product, but these three almost always deliver. I start with whichever fits your current bottleneck.
AI features inside the product
Embedded AI in the product itself moves the needle more than back-office automation. Summarisation (long inputs summarised on demand). Semantic search (search by meaning, not keywords). In-app assistants (help, onboarding, navigation via chat). Content generation (first drafts of outputs your product already makes). Data exploration (natural language queries against your product's data). Each is 2 to 4 weeks of work. For SaaS competing with AI-native rivals, embedded AI is often the differentiator that keeps you in the game.
My 30-day integration playbook
Week one: audit of current support tickets, user feedback, and product usage to identify highest-ROI AI opportunity. Week two: build the first AI integration end-to-end (LLM provider setup, prompt engineering, output validation, user-facing surface). Week three: internal test with your team, tune based on real outputs. Week four: ship to production with monitoring and rollback path. Single focused 30-day sprint beats 6-month AI transformation programs that never ship. Subsequent months iterate on the first feature and add new ones based on data.
Pricing — $3,000/mo retainer
$3,000/mo single tier. Covers AI integration work, prompt engineering, monitoring setup, and ongoing tuning. 14-day money-back guarantee. Cancel anytime. 100 percent code ownership under Work Made for Hire. LLM API costs pass through — typically $50 to $500/month depending on usage. For SaaS with heavy AI usage, cost management is part of the monthly work (prompt compression, model routing, caching). I do not hold your API keys; the accounts (OpenAI, Anthropic, others) are yours. You can cancel the retainer without losing anything.
Case: Cuez and Instill
Cuez: broadcast-SaaS API from 3 seconds to 300ms, 10x faster, ~40 percent infra cost reduction (Laravel, Vue.js, TypeScript, AWS, FFMPEG). Performance discipline transfers to AI — well-designed AI features are fast, reliable, and cost-controlled. Instill: self-initiated AI skills platform with 30+ users, 1,000+ skills saved, 45+ projects powered (Next.js 16, React 19, TypeScript, PostgreSQL, Vercel, MCP Protocol). Built solo, demonstrates the structured-prompt + AI-generation pattern that most SaaS AI features benefit from. Both inform the work I ship for SaaS clients.
When hiring an ML engineer is better
Hire an ML engineer full-time when you need custom model training, domain-specific fine-tuning, or real-time ML serving at scale. My retainer is for practical LLM-based automations — prompt engineering, RAG architectures, workflow integration. That covers 80 percent of what most SaaS companies need today. For AI-native products where AI is the core value proposition (like genuinely building a new model), full-time ML engineering from day one. For everyone else, an AI retainer unblocks product velocity without the hiring overhead. I help you decide in the first call.
Recent proof
A comparable engagement, delivered and documented.
A prompt library that works with every AI tool
A home for your best AI prompts. Save them once, then use them in Claude, Cursor, or any AI tool you work with. No more copy-paste.
Frequently asked questions
The questions prospects ask before they book.
- What about data privacy?
- Default pattern: use enterprise tiers of OpenAI or Anthropic with data-processing agreements ensuring prompts are not used for model training. For SaaS with sensitive customer data (PII, PHI, financial), we use tiers with no-data-retention policies. For extremely sensitive workloads, self-hosted open-source models (Llama, Mistral) on your own infrastructure. Model choice depends on sensitivity and cost tolerance. Documented in privacy policy and customer-facing DPA.
- OpenAI, Anthropic, or something else?
- Default: Anthropic Claude for reasoning and long-context tasks (summarisation, analysis, first drafts). OpenAI for embeddings and structured output. Both integrate cleanly, cost comparably for most use cases. For budget-sensitive workloads, open-source models (Llama via Replicate, Groq) can cut costs 5 to 10x with some quality tradeoff. I pick based on task, sensitivity, and budget — not based on brand preference.
- How do you handle evals?
- Start with golden-set evals (20 to 50 hand-crafted test cases with known-good outputs) for each AI feature. Run evals on every prompt change to catch regressions. For production systems, lightweight monitoring tracks output quality through sampling. For high-stakes outputs (customer-facing responses, financial decisions), human-in-the-loop review with feedback captured for ongoing tuning. Full eval infrastructure (LangSmith, Braintrust) for SaaS running many AI features in production.
- How much do API calls cost?
- Depends on usage. Typical SaaS starting AI: $50 to $500/month in API costs. Heavy users: $1,000 to $10,000/month. Cost optimisation is part of the retainer — prompt compression, model routing (cheaper model for simple tasks), caching, RAG to reduce context. For SaaS with per-customer AI usage, per-seat pricing models align cost with revenue. I help structure pricing if AI features become a premium tier.
- Can we self-host?
- Yes, for workloads where data sensitivity or cost makes hosted APIs unattractive. Self-hosted options: Llama 3/4 via vLLM or Ollama, Mistral, Qwen, other open-source models. Hardware: AWS or GCP GPU instances, or dedicated hardware. Quality gap vs frontier models is narrowing — for many tasks, open-source models are good enough. Cost tradeoff: self-hosted is cheaper at scale (1M+ requests/month) but has operational overhead hosted APIs do not. Decision is case-by-case.
Ready to start?
Tell me what you need in 60 seconds. Tailored proposal in your inbox within 6 hours.