Platform architecture, AI features, and growth systems. For SaaS teams that ship.
We build the infrastructure, AI layer, and growth engine that SaaS products run on.
SaaS companies need to ship AI features fast, scale infrastructure without downtime, and grow efficiently. We build the platform architecture, AI integrations, and growth systems that let product teams move at startup speed with enterprise reliability.
Why AI in SaaS is different.
Every SaaS product now has "AI-powered" on the roadmap. Over 60% of SaaS companies plan to embed AI features within 12 months. Most will ship a chatbot wrapper and call it done. The products that win will embed intelligence into the core workflow, not bolt it on as a sidebar.
The real challenge is not building the model. It is building the infrastructure around it: multi-tenant data isolation, usage-based billing for AI features, latency requirements under 200ms, and SOC 2 controls that satisfy enterprise procurement. 45% of enterprise deals stall on security questionnaires the engineering team cannot answer.
Speed is the moat. The SaaS companies pulling ahead ship AI features in 4-6 week cycles. The ones falling behind are still debating build vs. buy. We help you skip that debate and start shipping.
The Problems That Slow You Down
Scaling architecture hits walls at growth
Single-tenant designs that worked at 100 customers break at 1,000. Database queries slow, costs spike, and the refactor keeps getting pushed.
AI features are on the roadmap but never ship
Product wants AI recommendations, smart search, content generation. Engineering is 6 months behind on core features.
Churn signals are invisible until it's too late
Usage drops, support tickets spike, renewal dates pass, and the data to predict it sits in 5 different tools.
Enterprise deals stall on compliance
SOC 2 Type II, SSO, data residency: every enterprise prospect has a 40-item security questionnaire your team can't answer yet.
Systems for SaaS
Multi-Tenant Architecture
SaaS platforms built for scale: tenant isolation, per-customer configuration, billing integration, and white-label support.
View service →AI Feature Integration
Recommendation engines, smart search, content generation, and analytics, shipped as product features, not science experiments.
View service →Product Analytics Infrastructure
Event pipelines, cohort analysis, and churn prediction dashboards that product teams actually use to make decisions.
View service →SOC 2 & Enterprise Readiness
Compliance controls, SSO, audit logging, and data residency, everything enterprise buyers need before they sign.
View service →These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.
What We Build in SaaS
Predictive User Churn
Head of Customer SuccessRetention teams miss 20% monthly churn signals across 100K users. $500K+/month lost MRR for $20M ARR SaaS.
XGBoost on behavioral cohorts (feature adoption, session depth, support tickets) + time-series decay, Snowflake/BigQuery integrated.
Lead Scoring Prioritization
VP of Sales OperationsSDRs chase 80% low-fit leads from MQL floods (5K/month); sales cycles 47 days, 4% conversion.
Random Forest predictive scoring on 200+ firmographics/behaviorals, CRM-integrated with auto-routing.
Support Ticket Agent
Director of Customer SupportTier-1 reps handle 6K tickets/day manually; 40% FRT delays, 25% NPS drop, $300K/month ops cost.
LLM agent (GPT-4o+RAG) triages/routes via NLU on Zendesk data, drafts resolutions from KB, escalates complex.
Infra Anomaly Detection
Head of SREDevOps misses 70% metric spikes post-deploy; 2+ hours MTTR, noisy alerts from 100s of metrics.
Isolation Forest unsupervised on APM traces (response time, CPU, throughput), context-aware baselines via feature flag integration.
Code Review Automation
CTOEng leads block 40% of 500/week PRs on style/security; velocity down 20% for 200-dev teams.
LLM agent scans diffs for patterns/bugs, suggests fixes via GitHub Action, trained on repo history.
Feature Request Analysis
Head of ProductPMs manually cluster 2K support/forum requests/quarter; roadmap delayed 4 weeks, 30% misprioritized.
BERT topic modeling + LLM summarization on Zendesk/Intercom data, sentiment-ranked for product board.
Expansion Opportunity ID
VP of Account ManagementAMs overlook 60% upsell signals in usage spikes/seat growth; 25% ARR expansion untapped.
Graph ML on account graphs (usage trends, modules), propensity scoring to Gainsight.
Self-Service KB Generation
Director of Product EnablementStale KB causes 50% ticket escalation; $150K/quarter in support scale costs.
LLM RAG auto-generates/updates articles from resolved tickets + changelogs, A/B tested in help center.
Building the system is half the job. Growing the business around it is the other half.
How We Grow SaaS Brands
AEO for SaaS Feature Comparisons
Head of GrowthB2B SaaS loses 40% of "best CRM for startups" to ChatGPT/Perplexity summaries without links.
Citable framework pages (structured tables, FAQs, schema), third-party signals (G2/Reddit), AI visibility tracking.
LinkedIn Ads + HubSpot Nurture for Enterprise SaaS
VP Demand GenLinkedIn CPL $110 with under 10% SQL rate; long cycles waste SDR time.
ICP-targeted video ads to HubSpot webinars/forms, behavioral scoring/nurture, Salesforce sync.
HubSpot Lead Scoring for Trials
Marketing Ops Director3K leads/month, 60% unqualified; no scoring delays trials.
HubSpot ML scoring (visits, downloads), nurture drips, demo booking integration.
AI Ads for Freemium Upgrades
Performance Marketing LeadGoogle CPC $5-10 for "free project tool" with 1-2% paid conversion; no Gemini presence.
Gemini/ChatGPT ads + Google PMax, attribution via GTM.
How We Work With SaaS
Platform Engineering
SaaS architecture, internal tools, API ecosystems: built for what comes after the MVP.
Learn more →AI Systems & Agents
LLMs, agent chains, and intelligent workflows, wired into your ops, not sitting in a sandbox.
Learn more →Data Engineering & Analytics
Your data is scattered across 15 tools. We wire it into one system that answers questions.
Learn more →Cloud & Infrastructure
Cloud architecture, migration, Kubernetes, monitoring: the foundation under everything else.
Learn more →Frequently asked questions
Yes. We embed with your engineering team. We read your code, follow your conventions, and ship PRs that your team can maintain after we leave.
We design for it from the data model up: tenant isolation, per-customer config, usage metering, and billing hooks. Not bolted on after launch.
That's the most common engagement. We add AI capabilities to your existing product, including recommendations, search, and automation, without touching your core architecture.
AI moves fast. Stay ahead.
No spam. One actionable email per week on AI systems and growth.
Related Answers
Should we build a custom AI system or buy an off-the-shelf tool?
If a SaaS tool does 80% of what you need, buy it. If your competitive advantage depends on the other 20%, build it. Here's the decision framework.
5 min readanswerHow long does it take to build an AI system?
A proof of concept: 2-4 weeks. A production system: 8-16 weeks. The real variable isn't the AI. It's your data.
3 min readYour product roadmap is 6 months long. We can compress it.
Start with a technical architecture review. We'll identify what to build, what to buy, and what ships fastest.