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Mosaic AI

Databricks

AI Aggregators
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Mosaic AI brings model development, retrieval, and deployment together on a single stack. Fine-tune or distill base models with your data, index knowledge in vector stores, and compose tools and reasoning into agents. Secure serving scales with autosync to data, while evaluations and traces reveal failure modes. Guardrails, permissions, and lineage make enterprise governance practical without slowing delivery. Templates accelerate Q&A, summarization, and ticketing, and APIs fit existing CI/CD.

Features

1

Training, Fine-Tuning, and Distillation

Adapt foundation models with domain examples, instructions, and feedback. Parameter-efficient methods cut cost while preserving quality, and curation tools remove leakage and bias before training begins. Curriculum runs and checkpoints keep progress measurable, and reproducible configs let teams compare setups without drift. Artifacts, prompts, and datasets are versioned together so experiments are auditable. Canary rollouts and staged releases protect users while improvements prove themselves.

2

Retrieval and Vector Search

Embed documents, tables, and events, then query with hybrid search that blends vectors and keywords. Chunking, metadata, and freshness filters improve grounding so answers cite sources and track what’s current. Synonym maps and reranking tighten relevance when language varies across teams, while index health metrics reveal when to re-embed, prune, or expand. Scheduled refresh keeps collections aligned with changing knowledge, preserving trust as policies, prices, or content shift.

3

Agents, Tools, and Orchestration

Compose multi-step plans that call tools like databases, code, and external APIs. State and memory maintain context across turns, and policies constrain actions so agents stay safe, reversible, and auditable. Human-in-the-loop stops and approvals catch edge cases, while traced replays explain why each action occurred. Shared templates standardize patterns such as research, triage, and ticketing. Dashboards surface failure classes so fixes target prompts, tools, or routing instead of guesswork.

4

Serving, Monitoring, and Evaluation

Serve models with autoscaling, caching, and routing by policy. Golden sets, offline evals, and live A/Bs watch accuracy, latency, cost, and drift so regressions are caught before customers feel them. Structured feedback and red-team prompts feed a loop where issues become measurable improvements, not recurring incidents. Alerts integrate with on-call tools, while rollbacks are one click when a change misbehaves. Clear SLAs make reliability predictable as usage grows across teams.

5

Security, Governance, and Lineage

Control access with roles and secrets; encrypt data in transit and at rest. Track datasets, prompts, and models across versions to explain outputs and satisfy audits. PII scanners, content filters, and rate limits prevent abuse without breaking legitimate use. Exportable logs show who changed what and when for compliance. Lineage ties sources to decisions so stakeholders see why an answer was produced, and retention policies keep sensitive inputs within approved boundaries.

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Recomended For

Recommended for product, data, and platform teams shipping AI features with regulated or dynamic content. Mosaic AI merges training, retrieval, agents, serving, and governance so prototypes become maintainable apps. Leaders see cost and quality tradeoffs clearly; engineers iterate safely with evidence; and stakeholders trust results because sources, tests, and lineage are visible. Vendor-neutral connectors avoid lock-in, and SLAs keep reliability predictable as scale increases.

What it solved

AI efforts often stall between demos and production. Mosaic AI provides the path: adapt models, ground responses, orchestrate tools, and monitor outcomes in one workflow. The impact is lower risk and faster iteration—teams measure behavior, enforce policy, and update knowledge without rewrites when requirements change. Continuous evaluations surface regressions early, rollbacks are straightforward, and answers age gracefully because retrieval refreshes on schedule.

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