Generative LLM Systems

Enterprise LLM integration focusing on RAG and fine-tuning. We anchor AI outputs against your proprietary knowledge bases to eliminate hallucinations and secure private data.

What We Build With It

LLM systems designed for accuracy, latency, and cost targets.

Grounded Question Answering

Answers tied to your documents with source visibility.

Semantic Search and Discovery

Search that understands meaning across large text collections.

Content Generation with Guardrails

Drafts and summaries constrained to approved formats.

Document Processing

Structured extraction from unstructured documents at scale.

Conversational Interfaces

Chat experiences with context, memory, and escalation paths.

Workflow Integration

LLM capabilities embedded directly into business processes.

Why Our Approach Works

We design for production failure modes from day one.

Privacy by Architecture

Access controls and audit trails built into the system.

Grounding and Validation

Outputs are traceable and verified before they act.

Predictable Economics

Right-sized models, caching, and batching keep costs stable.

How We Build Generative Systems

Production architecture your team can run and evolve.

Model Selection

Models matched to accuracy, latency, and data sensitivity.

Retrieval Layer

Semantic indexing that finds the right context fast.

Prompting and Tuning

Systematic prompts and tuning for consistent outputs.

Serving Infrastructure

Caching, rate limits, and failover for reliable delivery.

Safety and Governance

Policy enforcement and review paths for sensitive actions.

Evaluation and Monitoring

Quality checks and drift monitoring over time.

Harness Generative AI

Work with Metasphere to integrate safe and effective LLM solutions into your operations.

Explore GenAI Solutions

Frequently Asked Questions

How do you reduce hallucinations?

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We ground answers in your sources, constrain outputs, and validate before actions occur.

Can we keep sensitive data private?

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Yes. We design for data isolation, access controls, and auditability.

What does it cost to run?

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Costs depend on volume, model choice, and latency. We model spend up front and monitor it in production.

Do we need a semantic index?

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If you want reliable answers from your documents, yes. It provides accurate retrieval beyond keyword search.

How long until we have something usable?

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Focused pilots can ship in weeks. Production hardening takes longer and should be staged.