AI for SaaS Companies — build the AI features your roadmap can't wait for.

Outbound SDRs plateau at ~150 quality-scored conversations a month per rep; cost-per-opportunity hovers in the $400–800 band and the CFO has noticed.

An autonomous agent enriches each lead from Apollo, Clay, and public signals (LinkedIn, GitHub, recent funding), drafts a multi-touch sequence personalised from those signals, handles the first two reply turns, and only hands off to a human AE when an intent score crosses a tuned threshold. The reps stop writing the same intro email 80 times a week and start showing up to calls that already self-qualified.

Product, engineering, and CS burn 4–7 hours a week digging through Notion, Slack, Linear, and Looker for context that already exists somewhere. The new hire ramp is brutal because nothing's findable.

We ship a RAG-grounded copilot over your knowledge graph with strict source citations and tool-calling into Linear, Notion, and Jira for live data. Cohere Rerank 3.5 keeps retrieval honest; the agent refuses out-of-corpus questions instead of guessing. The eval set covers your 30 most common ICP questions, graded by your team — not a vendor's generic benchmark.

T1 support handles 60–75% of tickets that genuinely could be solved by self-service — if the docs were navigable and the billing API were reachable. They aren't, so the queue grows.

A grounded chatbot inside your product, plus an email auto-responder, retrieves from your help center, drafts the reply, calls Stripe and your session APIs for live account data, and escalates to a human the moment confidence drops. CSAT holds inside ±2 points because the agent refuses cleanly instead of bluffing. We wire it into Zendesk or Helpscout so the human gets full context, not a transcript dump.

Revenue ops sees churn signal roughly two weeks too late. The CSM intervention email goes out after the renewal date is already at risk, and the playbook for what to send is a Google Doc nobody opens.

We pair a classic ML model — gradient-boosted tree on usage features — with an LLM outreach agent that drafts the CSM's pre-approved intervention message. The model is XGBoost or LightGBM with MLflow versioning; the feature pipeline runs on dbt against Snowflake. The agent never sends without CSM approval. It just removes the blank-page problem.

PMs and execs have BI dashboards, but every ad-hoc question ("which features drive 30-day retention in the enterprise tier?") still queues behind the data team for 1–3 days. Analysts become the bottleneck on every roadmap decision.

A natural-language layer sits on top of a governed semantic layer — Cube or dbt Semantic Layer — so the agent translates English into validated SQL against curated tables, not raw schemas. It returns the number, the chart, and the SQL it ran so the data team can audit. Guardrails: column allow-lists, row-level security, and query-cost caps. We benchmark accuracy with the defog or Vanna eval suite before it ever ships to non-technical users.

Account managers miss 30–50% of expansion-ready accounts because seat saturation, module adoption velocity, and feature-flag usage live in three different tools and nobody's correlating them weekly.

Lightweight anomaly detection runs over usage data to rank accounts by expansion-readiness; an LLM drafts the account brief for the AM — "here's why this account is ready, here are the two product surfaces they're saturated on, here's the recommended motion." The brief lands directly in Salesforce or HubSpot inside the AM's existing workflow. We've found AMs ignore anything that requires a second tab.

AI-feature parity is table stakes in 2026. Building those features in-house takes 4–6 months per surface and burns the roadmap your customers actually asked for.

We drop AI features straight into your product: RAG search over user content, document summarization, structured extraction from emails and PDFs and screenshots. The integration uses Vercel AI SDK or LangChain.js on the frontend, Claude Sonnet 4.6 or GPT-5 for inference, Pinecone for retrieval, and Anthropic's structured-output mode for the extraction calls. Eval gates fire before any feature touches production users.

Complex B2B SaaS products lose 40–60% of trial signups before activation. Manual onboarding doesn't scale past Series A, and self-serve checklists don't adapt to the customer's actual job-to-be-done.

A conversational onboarding agent guides the user through setup, asks targeted scoping questions, configures the product against your APIs, and drafts the first templates or content so the user hits value on day one, not day fourteen. The agent runs on Claude Sonnet 4.6 with tool-calling against your product's internal APIs; the UI is a thin Vercel AI SDK layer inside your existing onboarding flow.