Reliability-first retrieval
Right context for AI.
Minimize context loss. Answers backed by citations.
Datester builds retrieval infrastructure for AI systems, starting with transcripts. We preserve context integrity, reduce context loss across chunking and retrieval, and return citation-backed evidence so models can answer grounded in source material.
Right context for AI. Minimize context loss. Verifiable answers with citations.
Why retrieval fails today
Most LLM failures are not model failures, they are context failures. Chunking and retrieval pipelines often discard relationships that carry meaning, which leads to ungrounded answers.
- Naive chunking splits key statements from their qualifiers, owners, or timestamps.
- Retrieval returns partially relevant text windows that miss the decisive context.
- The model fills gaps with plausible language, not necessarily evidence from source material.
- Teams lose trust because they cannot trace outputs back to auditable references.
Reliability-first retrieval for grounded AI answers
Datester is built around context quality, evidence quality, and output traceability. The result is tighter context windows and citation-backed responses teams can verify.
Context-preserving segmentation
Segmenting transcripts with boundary awareness so key meaning and dependencies remain intact.
Evidence-first retrieval
Ranking for evidentiary value, not just lexical overlap, to prioritize trustworthy context.
Citation-backed responses
Answer payloads include source anchors so reviewers can inspect exactly why a claim appears.
Minimal context waste
Tighter, relevant context windows reduce token waste while preserving reasoning-critical details.
How it works
A practical retrieval pipeline designed for traceability from ingestion to final answer.
Ingest → Normalize → Segment → Index → Retrieve → Answer with citations
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Ingest + NormalizeCollect transcript inputs and standardize structure, speakers, and timestamps.
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SegmentCreate context-preserving chunks that retain semantic dependencies.
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IndexStore enriched segments with metadata needed for precise retrieval.
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RetrieveAssemble tight evidence windows with strong relevance and minimal context waste.
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Answer with citationsReturn grounded outputs linked to the exact source evidence used.
Evidence chain: source text remains visible and reviewable all the way to the generated answer.
Trust and reliability, built into the pipeline
Datester helps teams move from opaque outputs to accountable retrieval and answer generation.
Grounded responses over unsupported fluency
Prompt contexts are selected for evidence quality so outputs are tied to source material, not just likely phrasing.
Traceable from answer back to source
Teams can inspect citation anchors and evaluate whether each answer is justified by the retrieved evidence.
Reduces hallucination risk
By improving context integrity and retrieval precision, Datester lowers the chance of unsupported claims.
Use cases
Built for teams that need actionable answers tied to evidence.
Consulting notes → action items
Convert dense discussion notes into next steps linked to the exact source rationale and owners.
Meeting transcripts → decisions + follow-ups
Extract decisions, blockers, and commitments with references teams can quickly verify.
Internal docs → policy Q&A with citations
Enable staff to ask policy questions and see exactly which document sections support each answer.
Roadmap
Current focus is transcripts, with broader retrieval coverage and controls in progress.
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Now
Transcripts reliability layer Context-preserving segmentation and citation-backed answer flows for transcript-heavy workflows.
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Next
PDF and document support Expand ingestion and retrieval quality controls across mixed document formats.
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Soon
Evaluation harness Repeatable tests for context integrity, evidence coverage, and citation quality.
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Later
Enterprise controls Policy controls, audit workflows, and reliability instrumentation for production deployments.
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