Talk to your data
Chatbots that answer instantly from your data, giving every team precise, context-aware responses grounded in your own sources.
Retrieval-augmented generation
★★★★★
4.8 / 5
★★★★★
4.8 / 5
Enterprise-ready RAG for data extraction.
Chat with your data. Forage AI builds retrieval-augmented generation systems that turn scattered documents, web sources, and databases into instant, accurate, cited answers.
Any data type
Text, tables, images, A/V
Grounded answers
Cited to your sources
Model-agnostic
Plug in the latest LLMs
Your infrastructure
Data never leaves
Beyond basic search
How our RAG truly understands your data.
Eight engineering choices separate real understanding from keyword lookup.
Supercharged text extraction
Pulls information from unstructured text, images, charts, audio, and video into one unified framework.
Dynamic chunking
Handles large datasets efficiently while preserving the quality and precision of every passage.
Contextual prompting
Tailors each response precisely to the query, producing accurate and contextually relevant insights.
Re-ranking for optimal results
Continuously refines search results, so answers grow sharper and more meaningful with every interaction.
Multi-index synergy
Integrates data across your data lake through multiple indexes for cohesive, comprehensive insights.
Knowledge graph search
Uncovers sophisticated data relationships that basic search patterns never see.
Self-evaluation and refinement
Reviews its own outputs continuously, adapting over time toward logical, high-quality answers.
Plug & play the latest LLMs
Swaps in the most advanced models as they evolve, keeping performance at its peak.
Need RAG on your own data?
Every pipeline is custom-built around your sources.
How it works
Answers that stand on your data, end to end.
Follow one question through the pipeline. Each stage is tuned to your data, so the answer arrives accurate, cited, and ready to use.
Connected
One pipe in.
Approved websites, filings, PDFs, tickets, and databases flow into a single dataset, with your permissions preserved from day one.
Current
An index that stays fresh.
New and changed content is picked up and re-embedded automatically, so every answer reflects what your sources say today.
Focused
From thousands to the vital few.
A query fans out across the whole index, and only the strongest passages continue on to the model.
Linked
Context, not just matches.
When the answer depends on how facts relate, connected entities supply the missing context automatically.
Cited
Every claim has a receipt.
The answer is composed from the retrieved passages, checked against them, and shipped with its citations attached.
Live
Where your team already works.
Chat, search, or API: responses arrive inside your applications and never leave your environment.
RAG pipelineanswering
▸connect approved sources
→web · documents · databases
→permissions mapped
✓data sources connected
▸refresh the index
→312 changed pages detected
→embeddings updated
✓index current
▸query: "what changed in Q3?"
→40 candidates retrieved
→narrowed to the best 4
✓evidence locked
▸expand the context
→entities linked across sources
→related filings attached
✓context complete
▸compose the answer
→drafted from evidence only
⚑one weak claim dropped
✓citations pinned per claim
▸deliver the answer
→chat · search · API surfaces
→inside your environment
✓answered in 1.2s
Why Forage AI
The data layer and RAG pipeline, managed by one team.
The data layer is in-house. Source discovery, web extraction, document processing, and monitoring feed your knowledge base, so retrieval starts from dependable data.
Hallucination-resistant by design. Every response is grounded in factual data from your databases and knowledge graphs, with contextual prompting and re-ranking tuned for accuracy.
Secure in your infrastructure. Your data stays in your environment, and role-based permissions carry through to every retrieval and answer.
Experts in the loop. Solution architects and reviewers tune retrieval, handle edge cases, and validate outputs where accuracy matters most.
Managed from pilot to production. One team handles scoping, integration, calibration, deployment, and monitoring, while you keep full ownership.
API and SDK ready. Integrates into your existing applications with plug-and-play flexibility, so you stay current as LLM technology evolves.
Use cases
Smarter decisions across every workflow.
Put RAG to work wherever your team digs through data for answers: chat, search, review, and reporting on one grounded pipeline.
Semantic search
Search complex, unstructured data by meaning rather than keywords, and retrieve contextually relevant results in seconds.
Intelligent recommendations
Personalized suggestions that surface your most valuable insights, resources, and products from patterns in your data.
Policy review and audit support
Surface key compliance information, confirm alignment with regulatory standards, and move through vast repositories at audit speed.
Document review at scale
Extract and synthesize crucial information from legal, technical, and financial files, and query even the largest documents for precise answers.
A unified view of your data
Consolidate diverse sources into one queryable view that powers comprehensive reporting and confident decisions.
Resources
All resources →
Latest and hottest in RAG.
Researching your way through retrieval-augmented generation? Start here.
RAG is a strong fit when an application must answer questions from a large, changing, or proprietary body of knowledge and users need access to the supporting source.
A small and static collection may only require search or long-context prompting. Fine-tuning is more appropriate when the objective is to change model behavior, tone, or output format rather than provide current knowledge.
During scoping, Forage AI assesses whether the workflow requires RAG, a simpler retrieval layer, an AI agent, or a combination of approaches.
The dataset can include internal documents, knowledge bases, reports, PDFs, scanned files, websites, public portals, licensed sources, databases, and APIs.
Text, tables, page layouts, images, charts, metadata, and document relationships can be processed according to the needs of the workflow. Available coverage and source permissions are reviewed before implementation.
Yes. The architecture can be designed around the organization's current models, cloud environment, databases, vector store, frameworks, applications, and security requirements.
Technology selection is based on the required accuracy, retrieval complexity, latency, deployment model, and operating cost. Forage AI's current offering supports flexible model integration and connection through APIs and SDKs.
RAG reduces unsupported responses by grounding the model in retrieved evidence, but it does not guarantee that every answer is correct.
Evaluation begins with representative questions, expected sources, and agreed acceptance criteria. The workflow can measure retrieval relevance, evidence coverage, citation support, unsupported-answer rate, latency, and cost.
Confidence thresholds, abstention rules, structured outputs, and human review can be added for sensitive or high-impact use cases.
Forage AI can manage source collection, document processing, data refreshes, deduplication, metadata updates, reindexing, retrieval evaluation, and pipeline maintenance.
When a source changes, a document is replaced, or information expires, the relevant content can be reprocessed according to the agreed refresh and versioning rules. Performance can also be reevaluated as new questions, users, and source types are introduced.
Start foraging
Start getting answers from your data.
Tell us about your sources and the questions your team needs answered.
- Data audit on your actual sources
- A tailored RAG architecture scoped to your use case
- Pilot on your data before you commit
Get in touch