Durable, AI-First App Development

Agentic systems, grounded in ontology, that deliver trust.

Kilowatt Software designs, builds and operates AI-first products. We believe modern agentic systems only work when they stand on robust data, an explicit orchestration layer, and a well-designed ontology that gives agents something structured to reason over.

See the apps Our architecture
What we believe

Three things every serious agentic system needs.

LLMs alone don't make a product. They are the new utility. The systems that hold up in production share a common shape: strong data foundations underneath, an explicit, agentic orchestration layer in the middle, with UX driven by a context graph. This graph representation of a domain powers agents that plan, reason, and recover.

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Durable data foundations

Schemas, validation, lineage, and state that behave the same on day 1 and day 1,000. Agents are only as trustworthy as the data they read and write. Structured metadata is integrated into our context graph.

PostgreSQL Pydantic Event logs
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Agentic orchestration layer

Not a single mega-prompt and hallucinations. A layered runtime that routes between models, tools, and memory — so behavior is observable, testable, and cost-controlled. It executes well-defined, verifiable business logic.

AutoGen LangGraph MCP
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Graph-shaped knowledge & UX

We model domains as context graphs because in real workflows, direction of flow matters — prerequisites, dependencies, relationships, compliance. Neo4j sits behind our most ambitious agentic flows for a reason, enabling complex reasoning that integrates data and business logic to power AI-driven user experience.

Neo4j Cypher Semantic edges
Our specialty

Graph design for agentic orchestration.

Agents need somewhere to think. A well-designed context graph turns fuzzy domain knowledge into traversable structure with connected system and process knowledge — the orchestration layer uses it to plan next actions, check prerequisites, surface context, and keep multi-step work coherent.

  • Domain modeling first. We start by mapping nodes, edges, and invariants before a single prompt is written. The graph is the contract.
  • Neo4j as the semantic backbone. Core data, workflow dependencies, and system metadata live as first-class relationships — not buried in JSON blobs.
  • Orchestration that reads the graph. Agents query the graph to plan using complex reasoning — next action, which tool to call, and what context to load.
  • Performance and completion modeled explicitly. State, outcome distributions, and prerequisite chains are graph-native, not scattered across code bases and systems.
  • Observable by design. Every agent decision is traceable back to a node, an edge, or a rule — so failures are debuggable instead of mysterious. Graphs give certainty, agents give scale and flexibility.
Reference architecture
Layer 4
User Experience & Analytics
apps · dashboards · insights
Layer 3
AI Runtime & Orchestration
agents · routing · tools
Layer 2
Context Graph (Neo4j)
core data · workflows · metadata
Layer 1
Transactional Data (PostgreSQL)
users · events · analytics
Layer 0
Identity, Privacy & Compliance
policy · compliance · audit

Each layer is replaceable, testable, and observable. The graph layer is what makes the agentic layer above it behave predictably.

Portfolio apps

Products we're growing on this foundation.

Two AI-first products operated under Kilowatt Software — each a working example of data + orchestration + context graph done deliberately.

Portfolio app chipi.ai

Chipi.ai — AI Literacy for K–8.

Three-suite platform · 18 CoPilots · COPPA-aligned · K–8 focus
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Core focus

Chipi.ai teaches kids, families, and educators how to live and learn with AI — not just how to use it. Three specialized suites cover the whole learning ecosystem: TEACH for educators, LEARN for students, and LIFE for parents and families. The family-facing LIFE suite is the category's missing piece: no major competitor ships serious parent tools.

Eighteen CoPilots complement a tiered model system (Chipi Go → Pro → Prime), all powered by best-in-class open models in a controlled environment. A 12-cluster, ~150-module curriculum is embedded alongside core learning flows, and system metadata in Neo4j so progression, prerequisites, and mastery are first-class relationships rather than hard-coded flows. Student data is kept private, state is maintained, complex flows happen with low latency, and learning can be personalized.*

*Use of any Kilowatt Software application is subject to its terms and conditions and any commercial terms communicated at the time of agreement.

Why it matters for agentic design

Chipi is the clearest expression of our thesis: a graph-shaped curriculum drives an orchestration layer that routes learners to the right CoPilot, the right module, and the right model tier — with safety and COPPA guardrails enforced at Layer 0.

Portfolio app intmath.com

Interactive Math — an AI tutor built on real mathematics.

Categorical mathematics kernel · Wolfram-integration · Adaptive mastery modeling
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Core focus

We acquired the assets of Interactive Math to deploy an AI mathematics tutoring system designed from the inside out: its internal kernel is a rigorous categorical-mathematics framework, not just a generic LLM wrapper. Every tutoring interaction is grounded in a symbolic truth layer — either SymPy or Wolfram Alpha's CAG APIs verify results, computations, and hints so the tutor never "bluffs" a step.

Student mastery is modeled probabilistically, with integrated "memory" to ensure reinforcement and integrated learning. Each assessment is evidence that updates a learner's belief distribution over specific skills — which are themselves nodes in a prerequisite graph. The orchestration layer uses that graph to pick the next problem, the right scaffold, and when to step back.*

*Use of any Kilowatt Software application is subject to its terms and conditions and any commercial terms communicated at the time of agreement.

Why it matters for agentic design

Interactive Math shows what happens when a graph of skills and prerequisites sits between the learner and the model. The tutor doesn't have to "remember" anything implicitly — the graph remembers, and the agent reads from it.

How we build

Design, build, and harden AI-first products.

Discovery

Context design

We start with the user, their pain and the problem domain, not the stack. The first deliverable is almost always a graph schema and a set of invariants that the product must never violate, focused on user needs.

Build

Orchestration & agents

We build the orchestration layer — model routing, tool use, memory, guardrails — so agents are observable, testable, and cost-controllable from day one.

Operate

UX & data platform

Seamless experiences backed by transactional data, analytics, security, and compliance. We ship on top of enterprise-grade foundations, because that's where AI products actually live or die.

Want to learn more or talk AI?

We love to meet others driving AI-first experiences. If you want to chat or talk about commercial opportunities, reach out.

hello@kilowattsoftware.com See our apps