AI agents that live inside
your SaaS product.
In-product copilots, churn prediction, intelligent support routing, and data enrichment agents — built by a team that ships AI features to production, not just proofs of concept.
Where AI agents create product leverage.
Four agent types that consistently move the metrics SaaS companies care about — activation, retention, and expansion revenue.
In-Product AI Copilot
Before
Users struggle with complex features, rely on documentation, and submit support tickets for workflows they can't figure out. Power features have low adoption because the learning curve is too steep.
After
Context-aware AI copilot embedded in the product that guides users through complex workflows, generates content, answers product questions, and surfaces features they haven't discovered — all within the existing UI.
3x feature adoption
Churn Prediction & Intervention
Before
Customer success team manually reviews accounts quarterly. By the time they notice declining usage, the customer has already decided to leave. Renewal conversations start from a defensive position.
After
ML model monitors product usage signals, support ticket sentiment, and engagement patterns in real time. At-risk accounts are flagged 60-90 days before renewal with specific intervention recommendations — before the customer even thinks about churning.
35% churn reduction
Support Ticket Routing & Resolution
Before
Every support ticket goes into a single queue. L1 agents spend 40% of their time routing tickets to the right specialist. Resolution times average 18 hours. Complex issues bounce between teams.
After
AI agent classifies, prioritizes, and routes tickets based on content analysis. Simple issues are auto-resolved with verified answers. Complex tickets are pre-triaged with context summaries, reducing resolution time by 60%.
60% faster resolution
Data Enrichment & Insights Agent
Before
Users manually research and enter data into the platform. Records are incomplete, inconsistent, and stale. The product's value is limited by the quality of data users are willing to input.
After
AI agent automatically enriches records with external data, identifies patterns across the dataset, and surfaces actionable insights — turning your product from a system of record into a system of intelligence.
4x data completeness
Built for SaaS product and engineering leaders.
Head of Product
You're under pressure to add AI to the product but don't want to ship a gimmick. You need an AI feature that measurably increases activation, engagement, or retention — not a chatbot bolted onto the sidebar.
CTO
You need to evaluate build vs buy for AI capabilities, design a multi-tenant AI architecture that doesn't compromise data privacy, and keep model costs from eating your margins.
VP of Engineering
Your team is strong on backend and product engineering but doesn't have ML/AI expertise in-house. You need senior AI engineers who can integrate cleanly with your existing codebase and ship production features.
CMO / Head of Growth
AI is central to your positioning and competitive narrative. You need real, working AI features — not vaporware — that you can market, demo, and use to justify premium pricing tiers.
From product audit to production AI.
Product & Data Audit
We map your product's data assets, user workflows, and the highest-impact opportunities for AI. You get a prioritized feature backlog ranked by user value and technical feasibility.
Agent Architecture
We design the AI architecture — model selection, RAG pipeline, multi-tenant data isolation, guardrails, and the integration points within your existing product UI.
Beta & Feature-Flag Rollout
We build the agent behind feature flags, roll out to a cohort of power users, instrument every interaction, and iterate based on real usage data before a full launch.
Monitoring & Iteration
Post-launch we monitor adoption, accuracy, cost per interaction, and user satisfaction. We continuously improve the agent based on feedback loops and evolving model capabilities.
Questions about AI agents for SaaS.
Should we build an AI feature or a standalone AI product?
For most SaaS companies, embedding AI as a feature within your existing product is the higher-ROI path. An in-product copilot or intelligent automation leverages your existing user base, your existing data, and your existing distribution. It increases activation, reduces churn, and justifies premium pricing — all without the go-to-market cost of launching a separate product. A standalone AI product only makes sense when the AI capability serves a fundamentally different buyer persona or when the technical requirements (compute, data pipeline, latency) are incompatible with your core product architecture. We'll help you make this decision based on your product strategy, not the hype cycle.
How do you handle data privacy in multi-tenant AI implementations?
Tenant data isolation is non-negotiable. We design AI architectures where each tenant's data is logically or physically separated — no cross-tenant data leakage in training data, embeddings, or inference responses. For RAG-based features, each tenant gets their own vector store namespace. For fine-tuned models, we use per-tenant LoRA adapters or separate model instances. We implement guardrails that prevent the model from surfacing information from one tenant to another, even when sharing underlying infrastructure. All of this is documented and auditable for your SOC 2 and privacy compliance requirements.
How do you manage AI model costs at scale?
Model costs are the new infrastructure costs for SaaS companies, and they need the same FinOps discipline. We implement tiered model routing — using smaller, cheaper models (GPT-4o-mini, Claude Haiku) for simple tasks and reserving expensive models for complex reasoning. We add semantic caching to avoid paying for repeated queries, implement token budgets per tenant to prevent runaway costs, and monitor cost-per-interaction at the feature level. For high-volume use cases, we evaluate fine-tuning smaller open-source models that can run on your own infrastructure at a fraction of API costs. The goal is keeping AI cost per user under $1/month for most SaaS use cases.
How do you drive user adoption of AI features?
The biggest risk with AI features is building something technically impressive that nobody uses. We design AI features for progressive disclosure — they surface naturally in the user's workflow rather than hiding behind a separate 'AI' tab. Feature flags let you roll out to power users first, collect feedback, and iterate before a full launch. We instrument every interaction to measure adoption rate, task completion rate, and whether users return to the feature after first use. If adoption is below 20% after 30 days, we redesign the integration point rather than adding more AI capabilities. The feature has to reduce friction in a workflow users already care about.
Can AI features become a competitive moat for our SaaS product?
AI features become a moat when they improve with your data, not just with better models. A copilot that learns from how your users interact with your product — their workflows, their terminology, their edge cases — becomes more valuable over time in a way that a competitor can't replicate by simply plugging in the same LLM API. We design AI features to leverage your proprietary data assets: user behavior patterns, domain-specific knowledge bases, and feedback loops that continuously fine-tune the experience. The model is commoditized. Your data and your product context are not.
SaaS AI engineering — not generic chatbot vendors
We build AI features that ship to production inside SaaS products — not standalone demos. Our team understands multi-tenant data isolation, model cost optimization, and the product design required for real user adoption. You contract with a US LLC, work in your timezone, and get senior AI and ML engineers at 40-60% less than US-only rates through our LATAM delivery team.
Production AI, not proofs of concept
Multi-tenant RAG, guardrails, cost optimization, and feature-flag rollouts
US LLC + LATAM delivery
Senior AI engineers at 40-60% less than US-only rates, same timezone
Ready to add AI that makes your product indispensable?
Tell us about your product, your data, and where you see the highest-impact opportunity for AI. We'll show you what's technically feasible, what the model costs look like, and how to roll it out without disrupting your existing roadmap.