Adappt
Adappt works in Large Language Models for Commercial & Retail Banking, with further work in Predictive Analytics and AI Ethics & Governance.
- Average rating
- —
- Reviews
- 0
- Profile coverage
- 70%
- Evidence percentile
- P51
Profile updated · Request a correction
Company & commercials
- Primary category
- LLM Agents & RAG
- Team size
- Not disclosed
- Typical budget
- Not disclosed
- Primary industry
- Financial Services & Insurance
What Adappt does best.
LLM Agents & RAG provider for Financial Services & Insurance
Its own website shows its strongest capability in Large Language Models within LLM Agents & RAG, alongside Retrieval-Augmented Generation and Autonomous AI Agents. The industry it describes most is Commercial & Retail Banking.
Evidence report
How Adappt compares with peers on evidence, reviews and coverage.
What the evidence says
Adappt presents as a moderate evidence profile for LLM Agents & RAG. It ranks in the 51st percentile of 1,860 published providers in LLM Agents & RAG, and no reviews on the profile yet.
Current positioning: P51 evidence quality · P44 industry breadthAI capabilities.
AI Ethics & Governance
None of the listed technologies is specific to AI Ethics & Governance.
Mapping source · https://www.adappt.ai/solutions/ai-data-solutions — “the AI makes mistakes? All AI systems have confidence thresholds and human oversight for critical decisions. We design for graceful failure and continuous”
Featured 12 of 14 capabilities, ranked by mapping grade then confidence.
Industries served.
- Commercial & Retail BankingDirect · Primary95%
https://www.adappt.ai/ — “ice and the UK Government to the UN/WHO and major institutions across banking, law, and healthcare, we support work where failure can cost lives. A”
What clients say.
No independent reviews found for Adappt yet.
Worked with Adappt? Review them → Every reviewer is verified by RankGlobal.
How RankGlobal maps this profile.
RankGlobal maps this profile against a controlled taxonomy. Mapping grades on capability and industry cards describe how the profile is classified. Source strength for ranking decisions follows the hierarchy published on Research — a mapping grade is not a claim of discoverability weight.
- T1AuthoritativeTypical evidenceRegulators, registries, certifications, signed client evidence.Scoring useHighest evidentiary weight
- T2IndependentTypical evidenceCredible media, industry bodies, partner directories, third-party datasets.Scoring useStrong with attribution and recency
- T3Provider-controlledTypical evidenceCompany site, case studies, press releases, structured submissions.Scoring useAccepted as a claim; verification raises weight
- T4Discovery-onlyTypical evidenceDirectory listings, aggregations, and early discovery signals.Scoring useUsed for discovery; weighted accordingly in ranking
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- Primary category
- LLM Agents & RAG
- Primary industry
- Financial Services & Insurance
- Typical budget
- Not disclosed
- Team size
- Not disclosed
- 1Name the use caseSay which LLM Agents & RAG problem you are evaluating, so the research team can route your message accurately.
- 2Pressure-test the budget bandNo budget band is on file. Ask what a scope like yours typically lands at.
- 3Ask for a referenceRequest a client reference in Financial Services & Insurance — the strongest industry signal on this profile.
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