Grounded retrieval for domain search and AI answers

Agents use grounded retrieval to answer from your documents, with evidence. Hybrid search, ranking controls, and domain-tuned precision.

Platform

Retrieval that users can trust

  • Help search and answer tools work with your unique terminology, not just generic prompts.
  • Provide citations and sources so users can verify what supported a response.
  • Improve answer quality with ranking controls, evaluation, and tuning based on real corpora.

What better retrieval means for customers

It’s not just about ranking quality. It’s fewer misses, clearer evidence, and more useful answers.

01

Better domain retrieval

  • Hybrid retrieval uses both exact-match and semantic signals.
  • Metadata filters and ranking controls focus on actual business needs.
  • Tuning and evaluation improve results based on real data.
Playground search results with relevance scores and document provenance
02

Clear, trustworthy answers

  • Search and answer flows show exact citations and sources.
  • Single-query and batch workflows support product and operations.
  • Teams can show the source behind every answer.
Grounded answer with faithfulness score and cited sources
03

Quality you can measure

  • Automated relevance and groundedness scoring without manual labels.
  • Track quality over time. Know if last week’s change helped or hurt.
  • Dataset analysis identifies coverage gaps and domain patterns automatically.
Corpus health gauge showing A grade at 94 out of 100
04

Optimization that compounds

  • Synthetic query generation tunes retrieval weights automatically.
  • Swap LLM providers per request without changing application code.
  • Lower token cost by retrieving the right chunks first.
Optimization results showing 22 percent search quality improvement

What this looks like in a product

A user asks a specific question and gets a grounded answer

The experience should feel like a product search, not prompt roulette. Strong retrieval, short answers, and clear evidence.

  • Users ask specific questions and get a direct response based on their data.
  • Answers clearly show where the information came from.
  • Teams can improve ranking based on their query patterns, not default retrieval methods.

Example user experience

A support rep checks refund eligibility

The answer is direct, backed by data, and immediately useful.

Question

What’s changed in our refund policy for enterprise renewals?

Grounded answer

Enterprise renewals can still be refunded within 30 days when the account is under active review. Refunds above $10,000 now require finance approval.

  • Source: Billing policy v4.2
  • Source: Renewal exceptions memo
  • Updated: 2 days ago

Implemented with the Knowledge² Python SDK

Keep the implementation surface small

Python SDK example

Python
from sdk import Knowledge2k2 = Knowledge2(api_key="k2_...")results = k2.search( "corp_support_docs", "What’s changed in our refund policy for enterprise renewals?", top_k=5,)

Illustrative search response

JSON
{ "results": [ { "score": 0.93, "document_title": "Billing policy v4.2", "text": "Enterprise renewals may be refunded within 30 days when the account is under active review..." }, { "score": 0.89, "document_title": "Renewal exceptions memo", "text": "Refunds above $10,000 now require finance approval..." } ]}
  • Cited evidence on every answer
  • Tenant-scoped access controls
  • Audit logging
  • VPC / on-prem deployment
  • SOC 2 readiness

With Knowledge², our customers go from scoping to a working proof-of-concept in minutes, not weeks. Their technical and business teams move in lockstep instead of stalling on infrastructure. The ability to deploy within regions that have strict data residency requirements removes the last blocker to adoption.

Bruno Machado, CEOElevata