AI-native engineering studio

Your autonomous engineering department for AI products

We build and run AI-native systems like Webintell, Actyx, and RainmakerOS, then bring the same engineering firepower to ambitious B2B companies.

For VC-backed B2B SaaS (10-50M ARR), platform, DevOps/SRE, and growth leaders.

Mothership model

Geeksnova operates as the portfolio and services hub while each product retains standalone positioning and domain-level focus.

Makers of

Webintell

Agentic competitive intelligence for B2B SaaS.

by Geeksnova

Strategy

Actyx

Autonomous remediation for DevOps/SRE.

by Geeksnova

Reliability

RainmakerOS

Autonomous marketing engine.

by Geeksnova

Growth

We operate as both product foundry and embedded engineering department, shipping AI-native systems that actually run in production.

Our products

What we do

Products and engineering services under one roof

Geeksnova is the venture studio and engineering department behind standalone products and high-stakes B2B system delivery.

Webintell

Agentic competitive intelligence for B2B SaaS.

Actyx

Autonomous remediation for DevOps/SRE.

RainmakerOS

Autonomous marketing engine.

Engineering department-as-a-service

We embed senior engineers across product, infrastructure, and growth systems. Engagements are scoped to outcomes, not seat count.

Battle-tested on our own ventures, now available for you. Build new capabilities, stabilize production systems, and compound operational leverage.

Explore services

Products

Portfolio products with standalone brands

Each product is operated as an independent brand and domain, endorsed by Geeksnova.

VC-backed B2B SaaS founders

Webintell

Agentic competitive intelligence for B2B SaaS.

Turn fragmented market, customer, and GTM signals into a coherent strategic posture.

Problem: Teams get buried in fragmented competitor and market data.

Outcome: A unified strategic posture for product, GTM, and board-level decisions.

by Geeksnova

Platform teams, DevOps, and SRE leaders

Actyx

Autonomous remediation for DevOps/SRE.

Close the loop on incidents with policy-aware diagnostics and autonomous execution.

Problem: Alert fatigue and manual runbooks slow down incident response.

Outcome: Faster recovery with controlled automation and human-in-the-loop gates.

by Geeksnova

CMOs, growth, and revenue operations teams

RainmakerOS

Autonomous marketing engine.

Deploy agentic growth loops that run campaigns, learn, and improve with minimal lift.

Problem: Manual campaign ops and siloed data limit growth velocity.

Outcome: Compounding pipeline with automated orchestration and feedback loops.

by Geeksnova

Services

Four service pillars

AI product incubation, autonomous platforms, growth engines, and fractional engineering leadership.

AI product incubation

MVP to v1 with production-grade architecture.

Autonomous platforms & SRE

Observability, remediation, and resilient pipelines.

Agentic growth engines

Automation systems for high-intent B2B growth.

Fractional engineering department

Embedded senior team across product, AI, and infra.

How we work

Diagnose, design, operate

A production-first process: Diagnose and frame, Design and deploy, Operate and evolve.

1

Diagnose & frame

Model your technical and business system, constraints, and success criteria.

Deliverables

  • System map and risk register
  • Execution brief with milestones
  • Prioritized backlog and KPI baseline
2

Design & deploy

Ship the highest-leverage architecture and workflows in production-ready increments.

Deliverables

  • Reference architecture
  • Implementation slices with QA gates
  • Deployment and rollback strategy
3

Operate & evolve

Instrument outcomes, close feedback loops, and compound performance over time.

Deliverables

  • Observability dashboards
  • Reliability and change cadence
  • Quarterly evolution roadmap

Technical credibility

Production infrastructure and orchestration depth

Understated but serious systems work across platform reliability, agent runtime control, and operational observability.

Stack capabilities

  • Kubernetes and multi-region infrastructure
  • LLM orchestration and agent runtime policies
  • Event-driven pipelines and workflow automation
  • Observability, incident response, and SRE guardrails
  • Data platforms for analytics and adaptive learning loops

System loop

Signal ingest
→ classify and route
Agentic decisioning
→ policy and confidence gate
Remediation and orchestration
→ observability and learning loop

Proof

Proof over promises

When named case studies are unavailable, we use representative outcomes and operating patterns.

Representative outcomes: faster remediation loops and fewer manual handoffs
Representative outcomes: clearer product strategy alignment across leadership
Representative outcomes: tighter campaign operations with stronger signal quality

Representative outcomes

Platform reliability pattern

Representative outcome: moved from reactive alerts to policy-driven incident workflows with stronger operating clarity.

Signals

  • Shared operating cadence across teams
  • Improved system-level clarity and ownership

Representative outcomes

Go-to-market systems pattern

Representative outcome: replaced fragmented execution with a coordinated growth loop tied to shared funnel signals.

Signals

  • Shared operating cadence across teams
  • Improved system-level clarity and ownership

About

A small, senior, systems-first studio

We ship end-to-end across product, platform, and operations, with direct builder access and production accountability.

Work with us

Bring us in as your engineering department

Tell us what you are building and where you are blocked. We will respond with a direct technical perspective.

No spam, no automated sequences. A senior engineer will read this and respond directly.