Kensho
S&P Global's AI engine: connects LLMs and agents to trusted financial data through deterministic and adaptive retrieval.
If your workflow already runs on S&P Global data and you need it reaching LLMs or agents, Kensho is the shortest path — deterministic retrieval is the part generic AI data platforms struggle to match, because it returns exact, reproducible results rather than plausible-sounding ones. Link's coverage of 70 million companies via S&P Capital IQ Pro and BECRS, plus Extract's handling of complex PDF layouts, are real engineering, not prompt wrappers. The MCP Apps beta and Cohere North availability show Kensho meeting AI tools where they live. It is not a general AI platform: if you don't have an S&P Global data relationship, look elsewhere first.
Verified 16h ago · liveness 87/100 · cite: rightaichoice.com/tools/kensho
- Financial institutions needing AI-ready S&P Global data for LLM and agent workflows
- Enterprise AI developers building finance-specific applications on vetted, auditable data
- Analyst teams querying executive profiles and event intelligence in natural language
- Compliance-heavy desks needing traceable, reproducible retrieval for AI decisions
- Teams needing general-purpose AI without a finance-specific data focus
- Organizations without an existing S&P Global data relationship
- Users needing real-time data outside S&P Global's coverage
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Skip Kensho if you want a general-purpose AI assistant rather than finance-specific data infrastructure, or if you have no S&P Global data relationship to route into your models and agents.
Kensho runs on top of S&P Global data, so budget for the underlying subscription alongside the AI layer rather than treating it as a standalone tool cost.
Kensho is enterprise infrastructure priced for financial institutions and enterprise AI teams, and it assumes an existing S&P Global data relationship — so compare it against enterprise data platforms rather than against per-seat chat subscriptions. For a solo analyst or a small non-financial team, a general-purpose AI data platform will cost far less and cover the general case. The spend is justified when reproducible, finance-grade retrieval is the requirement, not when you just need an LLM
In short
Kensho — S&P Global's AI engine: connects LLMs and agents to trusted financial data through deterministic and adaptive retrieval. Best for Financial institutions needing AI-ready S&P Global data for LLM and agent workflows, Enterprise AI developers building finance-specific applications on vetted, auditable data, Analyst teams querying executive profiles and event intelligence in natural language. Contact Sales pricing.
What's new in Kensho
Checked todayAcross the latest 5 updates: 1 feature update, 1 launch and 3 news mentions.
Kensho LLM-ready API adds Key Developments and Professionals datasets
The LLM-ready API expands deterministic retrieval coverage with S&P Capital IQ Professionals data and Key Developments.
S&P Global partners with Cohere to bring data into North platform
Financial data now accessible inside Cohere's secure agentic AI platform North for research, analysis and reporting.
Kensho FIND dataset research accepted at ACL 2026
Kensho research shows top models surface over 60% of document inconsistencies, including errors authors missed.
S&P Global launches Adaptive Retrieval for AI and agentic workflows
Adaptive Retrieval joins Deterministic retrieval under the S&P Global AI Data Portal, targeting autonomous multi-agent systems.
RBC Capital Markets scales S&P Global and Kensho AI to 8,000 bankers
RBC integrated S&P Global data into its Aiden platform with Kensho Labs, scaling to 8,000 employees at 98% query accuracy.
Viability Score
How well maintained and how widely used is Kensho? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- S&P Global AI Data Portal connecting LLMs, agents, and AI applications to financial data
- Deterministic retrieval for exact, reproducible data queries
- Adaptive retrieval for exploratory AI workflows with underspecified questions
- Kensho Extract: turns unstructured financial PDFs into machine-readable data
- Kensho Extract interprets complex page layouts for clean downstream AI inputs
- Kensho Link: matches company data to S&P Global IDs and global identifiers at scale
- Kensho Link link score quantifying match strength on messy or incomplete inputs
- Coverage of 70 million public and private companies via S&P Capital IQ Pro and BECRS
- NERD: entity extraction linking companies, people, and locations to underlying profiles
- Scribe: real-time earnings call transcription
- Find dataset for detecting inconsistencies within documents
- MCP Apps (beta) for visualizing and interacting with S&P Global charts and tables in Claude
- MCP Apps (beta) for visualizing and interacting with S&P Global charts and tables in ChatGPT
- S&P Global data available on Cohere's North platform for agentic financial workflows
- S&P Capital IQ Professionals and Key Developments datasets delivered via LLM-ready API
About Kensho
Kensho is S&P Global's AI engine, and its job is deliberately narrow: get trusted financial data into large language models, agents, and AI applications that need to be right. The centerpiece is the S&P Global AI Data Portal, which exposes that data through two retrieval modes — deterministic retrieval for exact, reproducible queries, and adaptive retrieval for open-ended exploration where the question isn't fully specified up front. Underneath sit Kensho's core AI building blocks. Extract turns unstructured financial PDFs into machine-readable data while interpreting complex page layouts. Link matches messy company data to S&P Global IDs and global identifiers at scale, covering 70 million public and private companies in S&P Capital IQ Pro and the Business Entity Cross Reference Service (BECRS) databases. NERD extracts entities from unstructured text, tying every company, person, and location to its underlying profile. Scribe transcribes earnings calls in real time. Find flags inconsistencies inside documents. Recent work pushes the data into mainstream chat surfaces: MCP Apps (beta) visualize and interact with S&P Global charts and tables inside Claude and ChatGPT, and S&P Global data is now on Cohere's North platform for agentic financial workflows. Everything runs under S&P Global's scale, security, and compliance umbrella. The audience is enterprise AI developers, financial institutions, and analyst teams working inside S&P Global data who can't afford generic chatbot answers on a compliance-sensitive desk.
Behind the Verdict
Kensho is best understood as plumbing, not a product you chat with. The S&P Global AI Data Portal is the entry point, and its two retrieval modes are the substance: deterministic retrieval returns exact, reproducible answers for queries you can specify precisely, while adaptive retrieval handles the open-ended questions where you don't know the right filter up front. That split matters on a compliance-sensitive desk, where an AI answer nobody can reproduce is worse than no answer. The core AI capabilities are the second layer. Kensho Extract converts unstructured financial PDFs into machine-readable data and interprets complex page layouts, which is the unglamorous work that determines whether downstream models get clean inputs. Kensho Link matches company data to S&P Global IDs and global identifiers at scale, with a link score quantifying match strength, and it handles messy or incomplete inputs — the use case is deduplicating a CRM or synchronizing new records against coverage of 70 million public and private companies in S&P Capital IQ Pro and BECRS. NERD links every company, person, and location in unstructured text to its underlying profile. Scribe handles real-time earnings call transcription, and Find targets inconsistencies within documents. The distribution story is the newest piece. MCP Apps (beta) let you visualize and interact with S&P Global charts and tables inside Claude and ChatGPT, and S&P Global data is available on Cohere's North platform for agentic financial workflows. That is Kensho acknowledging that analysts already live in chat interfaces and refusing to force a portal detour. Treat the beta label seriously, though — MCP Apps is explicitly flagged as beta in Kensho's own material. Where it doesn't fit: teams without an S&P Global data relationship, anyone needing general-purpose AI, and small-scale or non-financial projects where enterprise data infrastructure is overkill. The strength is the moat — S&P Global's vetted, auditable dataset combined with engineering built to keep it accurate — and the constraint is the same moat: this is finance-specific infrastructure, priced and packaged for institutions rather than individuals.
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Real-world workflow fit
Concrete scenarios for the personas Kensho actually fits — and what changes day-one when you adopt it.
You are building an agent that answers questions over S&P Capital IQ data. You wire the S&P Global AI Data Portal in with deterministic retrieval for exact reproducible queries and adaptive retrieval for open-ended screens.
Outcome: Your agent returns traceable, reproducible answers on compliance-sensitive questions instead of plausible-sounding guesses, because the retrieval layer is S&P Global's own data.
Your CRM holds millions of company records with inconsistent names and addresses. You run Kensho Link against S&P Global IDs and the BECRS database, using the link score to triage matches.
Outcome: Duplicates and inconsistencies surface automatically, and new records synchronize against coverage of 70 million public and private companies.
You work inside Claude or ChatGPT and need S&P Global charts and tables in the same window. You pull them in through MCP Apps (beta) rather than exporting and rebuilding.
Outcome: Charts and tables render and stay interactive inside your chat interface, so the S&P Global data meets you where you already work.
Use Cases
- Query S&P Global data inside Claude or ChatGPT and interact with the returned charts and tables via MCP Apps.
- Match millions of messy CRM records to S&P Global IDs with Kensho Link and surface duplicates.
- Convert financial PDFs and filings into machine-readable data with Kensho Extract for downstream AI.
- Link companies, people, and locations in news or research text to S&P Global identifiers with NERD.
- Transcribe earnings calls and investor meetings in real time with Scribe.
- Detect inconsistencies inside financial documents using the Find dataset.
- Annotate thousands of words in seconds to build enriched, S&P Global-backed JSON training data.
- Run agentic financial workflows on Cohere's North platform using S&P Global data.
Models Under the Hood
as of 2026-09-22
Limitations
- Kensho's scope is deliberately narrow: it is finance-specific data infrastructure, not a general AI platform, so teams without an S&P Global data relationship are not the audience and non-financial or small-scale projects will find it overkill.
- MCP Apps is explicitly labeled beta in Kensho's own material, so expect limited functionality or reliability on that surface.
- Scribe is aimed at earnings calls and investor meetings rather than general audio.
- The vendor's public pages do not document a pricing model, so evaluate cost as part of an existing S&P Global relationship rather than against standalone AI tools.
as of 2026-09-28
Verification history
We have re-verified Kensho 21 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
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- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where Kensho's pricing actually pencils out — and where peers do it cheaper.
Kensho is enterprise infrastructure priced for financial institutions and enterprise AI teams, and it assumes an existing S&P Global data relationship — so compare it against enterprise data platforms rather than against per-seat chat subscriptions. For a solo analyst or a small non-financial team, a general-purpose AI data platform will cost far less and cover the general case. The spend is justified when reproducible, finance-grade retrieval is the requirement, not when you just need an LLM
Setup time & first value
How long it actually takes to get something useful out of Kensho — broken out by persona, not the marketing-page minute.
Enterprise AI developers with an existing S&P Global relationship: fastest path to value, since the data connection is already in place and the portal exposes it directly. Data operations teams running Link against a CRM: longer, because the first pass is scoping and scoring match quality on messy inputs. Analysts using MCP Apps inside Claude or ChatGPT: quickest to try, though the beta label
Switching to or from Kensho
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From ad-hoc PDF parsing scripts: route financial PDFs through Kensho Extract to get machine-readable output that handles complex page layouts.
- →From manual CRM deduplication in spreadsheets: use Kensho Link against S&P Global IDs and BECRS to match at scale with a link score.
- →From generic LLM retrieval over your own document store: move to the S&P Global AI Data Portal's deterministic retrieval for exact, reproducible answers.
- →From standalone entity-extraction models: use NERD to link companies, people, and locations directly to S&P Global profiles.
- ↗To a general-purpose AI data platform: viable if you no longer need finance-specific, auditable retrieval and can accept looser provenance on answers.
- ↗To in-house retrieval over your own data warehouse: viable only if you can replicate S&P Global's entity coverage, which spans 70 million companies in Capital IQ Pro and BECRS.
- ↗To a consumer chat assistant: covers casual questions but drops deterministic, reproducible retrieval, which is the reason to run Kensho in the first place.
Integrations
Resources & Guides
- Resourcekensho.com
Enterprise AI Solutions for Finance | Deterministic and Adaptive Data Retrieval
Explore Kensho’s suite of AI solutions. From LLM-ready APIs and the Kensho Grounding Agent to core ML capabilities, we provide the infrastructure to power trusted, agentic AI in global business and financial services.
- Resourcekensho.com
Careers at Kensho | Join the Team Shaping Financial AI — Kensho | S&P Global’s AI Engine | Enterprise Data Retrieval
Build the future of agentic AI and machine learning. Explore open roles in engineering, research, and data science at Kensho’s Cambridge and NYC hubs and around the globe.
- Resourcekensho.com
Kensho News | Latest Updates in Financial AI & ML Innovation — Kensho | S&P Global’s AI Engine | Enterprise Data Retrieval
Stay informed on Kensho’s latest product launches, research breakthroughs, and partnerships within the S&P Global and AI ecosystems.
Tutorials & Learning
YouTube returned 6 videos for “Kensho”, and we withheld 6: 6 could not be judged, because “Kensho” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Kensho.
Official links
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