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Plenware

Technology and SaaS Companies

An engineering partner for teams putting AI inside their products.

LLM features, RAG architectures, multi agent systems, and AI infrastructure built to the standards of your existing product.
Assistant

The context

Software companies are under pressure to add AI capabilities to their products, but a demo built in a weekend is far from a feature that thousands of customers can rely on. Product teams need evaluation, cost control, tenancy aware data access, latency budgets, and graceful degradation.

Plenware works alongside product and engineering teams to design and build AI features that meet those standards. We bring experience in LLM integration, retrieval architectures, agent orchestration, and deployment, and we leave behind code and documentation your team can own.

Prototype to production

AI demos rarely survive real users, real data, and real scale.

Quality you can measure

Without evaluation, every model or prompt change is a gamble.

Cost and latency

Inference costs and response times can erode margins and experience.

Specialist capacity

Hiring experienced AI engineers takes time.

AI opportunities

  • LLM features embedded in existing product workflows
  • Multi tenant RAG with per customer data isolation
  • Agentic capabilities with tool use and human approval steps
  • Evaluation pipelines integrated into CI
  • Inference cost and latency optimization

Plenware solutions for technology & saas

  • AI feature development
  • LLM integration
  • AI SaaS products
  • RAG architectures
  • Multi agent systems
  • Custom software engineering
  • AI infrastructure and deployment

Example use cases

What this looks like in practice

Representative patterns we design for. They describe capabilities, not named client engagements.

In product assistant

A contextual assistant that answers questions about a customer's own data inside the product, with strict tenant isolation.

Workflow agent

An agent that completes multi step tasks in the product using its existing APIs, with confirmation before changes.

Evaluation harness

Automated test sets and scoring that run on every pull request touching prompts or models.

Business outcomes

What organizations aim to achieve

  • AI features shipped with production safeguards
  • Measurable quality across releases
  • Predictable inference cost
  • Internal team capability built along the way

Discuss your AI product roadmap.

Tell us about your technology & saas environment, systems, and goals. We will propose an approach that respects its constraints.