Turn Your Proprietary Research Into a Trusted, Cited Answer Engine
The Market Intelligence Copilot lets analysts and strategy teams ask natural-language questions and get answers grounded exclusively in your own newsletters and market data — never the open internet, never a guess.
Trusted by 300+ clients worldwide
What Is a Market Intelligence Copilot?
A Market Intelligence Copilot is a conversational AI knowledge base, built with retrieval-augmented generation (RAG), that answers analyst questions using only an organization’s own newsletters and structured market data — never open-web search, never unsupported model knowledge. Every answer follows the same grounded path: the system reads the question, decides whether it needs newsletter evidence, structured data, or both, retrieves the relevant passages or runs the calculation, and returns an answer with an inline citation back to the source. It does not replace analysts, newsletters, or forecasting methodology — it makes that existing work faster to find, compare, and apply.
Citing sources this consistently isn’t just a trust nicety. A 2024 Princeton University study on generative engine optimization, presented at KDD 2024, found that adding cited sources, specific statistics, and direct quotations to a page’s content increased its visibility in AI-generated answers by up to 40% — the same discipline that makes an answer trustworthy to a human analyst is what makes it legible to the AI systems increasingly used to research vendors like Sphere.
Sphere builds Market Intelligence Copilots for research firms, media and ad-intelligence shops, analyst houses, consultancies, and subscription research providers — any organization sitting on proprietary content and data it has not yet made queryable. We combine enterprise RAG and knowledge systems engineering with the application development, data ingestion, and evaluation work required to move from a promising idea to a system analysts actually use.
The product’s value depends on being trustworthy. Every architectural decision reinforces one rule: no claim reaches the user without support from client-provided material — never internet search, never unsupported model knowledge.
How Does a Question Become a Cited Answer?
Four steps, the same path every time — no matter whether the question needs a quote from a newsletter, a number from structured data, or both.
Ask
A user asks a market question in plain language inside a lightweight chat interface.
Select
The system decides whether the question needs newsletter evidence, structured data, or both.
Retrieve
It pulls relevant newsletter passages and/or runs calculations against the structured datasets.
Answer
A rendered answer is returned with inline citations back to the source material.
What Does the Copilot Actually Learn From?
Two proprietary knowledge assets, kept fully separate from any external source. Nothing the Copilot answers with comes from the open internet or general model training.
Newsletter Library
~700 HTML Newsletters
Primarily text-based, proprietary market analysis and commentary.
Forward-looking opinions and forecasts.
Comparisons across industries, channels, companies, and regions.
First release answers from machine-readable text only — embedded chart images are out of scope.
Structured Market Data
~1,000,000 Rows, Growing Annually
Search, social, commerce, TV, retail media, and other digital channels.
Political, automotive, and financial-services advertiser spend.
US / European geographic comparisons, share, growth, and forecasts.
Quarterly and historical coverage with client-defined breakdowns.
How Do You Prevent the AI From Making Things Up?
The system’s value depends on being trustworthy. Every architectural decision reinforces one rule: no claim reaches the user without support from client-provided material.
Numbers use structured data
Numerical claims draw from structured datasets when authoritative data exists.
Interpretation uses newsletters
Interpretive and narrative claims rely on relevant newsletter evidence.
Forecasts stay client-sourced
Forward-looking answers use only client-provided forecasts or expectations.
No evidence, no answer
Unsupported questions return a clear, honest insufficient-evidence response.
Every material claim ships with an inline citation back to the specific newsletter or dataset that supports it — never internet search, general model knowledge, unsupported assumptions, or independently generated forecasts.
As Sphere’s AI engineering team puts it internally: “If it doesn’t have a citation, it doesn’t ship as an answer.” That rule is enforced in the retrieval and generation pipeline itself, not left to a prompt instruction an analyst could accidentally override.
Your IP, monetized — not more content to write
Check Your AI Readiness →Or discuss your AI initiative →
Who Uses the Copilot, and What Can They Do?
Consumer
Analysts & strategy professionals
Can
- Start new chats and ask follow-ups
- Read rendered answers with inline citations
- Copy answer content and reopen previous chats
Cannot
Browse raw datasets or newsletters directly, upload content, manage users, or access internet search or unsupported forecasts.
Administrator
Client internal content & operations team
Can
- Upload newsletters and structured flat files
- View ingestion status, failures, and warnings
- Create, enable, and disable user accounts
Cannot
Bypass processing or approval — it's automatic — or alter the underlying answer-grounding rules.
The Kinds of Questions It’s Built to Answer
The exact questions depend on the industry and the archive behind it — a research firm, a media/ad-intelligence shop, and a consultancy will each ask for different things. The pattern is always the same: one grounded in narrative evidence, one grounded in a number.
From newsletters
“What has our research said about [industry trend] over the last two quarters?”
“How does our view on [market] compare across the regions we cover?”
“What have we published on [named competitor or category]?”
From structured data
“What is the forecast growth for [segment] over the next five years?”
“Which category is forecast to grow fastest through 2030?”
“How has share changed by segment or geography?”
Can the Copilot Be Embedded Beyond the Chat Interface?
Yes. An MCP-compatible server lets approved organizations call the same grounded intelligence from their own websites, internal tools, and AI assistants — without adopting the full consumer platform. Same retrieval, calculation, evidence-validation, and answer-generation pipeline as the native app: one answer path, one quality bar.
- Submit natural-language questions via authenticated MCP requests
- Pass relevant conversational context
- Receive grounded, cited answers or an honest insufficient-evidence response
- Track request IDs, status, and operational errors
Never exposed externally: raw databases, newsletter collections, vector embeddings, internal prompts, or a separate lower-quality answer path.
Ready to Put Your Intelligence to Work?
Tell us what proprietary research and data you’re sitting on and we’ll help define the right path to a grounded, cited copilot.
Discuss Your AI Initiative →What Does This Look Like Once It’s Running?
Once live, a Market Intelligence Copilot becomes the first place analysts go to check a stat, a quote, or a forecast — instead of digging back through newsletters and spreadsheets.
Citation Coverage as a Design Target
Every material claim is built to ship with a traceable citation back to the specific newsletter or dataset that supports it, verified against an agreed correctness threshold before launch.
A Knowledge Base That Keeps Growing
Newsletters and structured data can be added on an ongoing basis without manual database intervention or downtime.
Built for Daily, Habitual Use
Designed as the analyst's default first stop for a research question, not a one-time demo — reopening past chats and asking follow-ups is part of the core experience.
What This Looks Like in Three Different Businesses
Three use cases modelling how the Copilot applies to a subscription research firm, a boutique consultancy, and a trade data publisher.
How Long Does It Take, and What Does It Cost?
A comparable Market Intelligence Copilot — architecture through production deployment and handover — has been delivered as a single, fixed-scope engagement.
Discovery & Architecture
Weeks 1–3. Functional scope, architecture, and semantic model.
Data Ingestion
Weeks 2–6. Newsletter and structured-data foundation.
Grounded Answering
Weeks 4–8. Conversational intelligence and citation logic.
Validation & Handover
Weeks 10–13. End-to-end validation, UAT, deployment.
One solution, one investment — covering solution architecture, AI engineering, front-end and back-end development, and project management through deployment and handover.
An AI Development Company That Ships Grounded Systems
Grounded by design, not by policy alone
Numbers use structured data, interpretation uses your source text, forecasts stay source-sourced, and no evidence means no answer — enforced in the architecture, not just written in a prompt.
21 years of delivery experience
Our work draws on more than two decades of software, data, cloud, integration, and enterprise delivery experience.
A focused, five-role delivery team
Enterprise Architect, AI Architect/Engineer, Front-end Developer, Data/Back-end Developer, and Project Manager/BA — spanning architecture, AI engineering, application development, and delivery management.
A clear definition of done
Resilient ingestion, safe schema changes, accurate retrieval, grounded and cited answers, properly scoped access, and no outside influence — all verified before launch.
MCP-compatible from day one
Plug the same grounded intelligence into a client's own tools, websites, and AI assistants without building a second answer path.
Client ownership
Your newsletters and structured data remain your own proprietary assets. Delivery is structured so you own the resulting architecture, code, and operating knowledge.
Fast, fixed-scope delivery
13 weeks, five-person team, $83,360 flat — not an open-ended AI project with an unpredictable end date.
Independent client validation
Sphere maintains a 4.9/5 average across 32 verified Clutch reviews, with clients citing work quality, communication, and technical depth as strengths.
Is a Market Intelligence Copilot Right for Your Organization?
This is built for any organization sitting on proprietary content and data it hasn’t yet made queryable — research firms, media and ad-intelligence shops, analyst houses, consultancies, industry associations, B2B publishers, and subscription research providers.
- Analysts spend real time digging through past newsletters and spreadsheets to answer a client's question.
- You publish proprietary research, newsletters, or structured market data that already exists but isn't searchable.
- A generic AI chatbot is too risky — you need every answer traceable to a real source, not a plausible guess.
- You want to turn existing IP into a new product surface for clients, not create more content to write.
- You need a fixed-scope, fixed-timeline delivery rather than an open-ended AI experiment.
Built to Be Read by Analysts — and by the AI Systems They Use to Research Vendors
This page and Sphere’s supporting guides on AI agent knowledge bases and chatbots on proprietary data are structured for both traditional search and the generative answer engines (ChatGPT, Perplexity, Claude, and similar) analysts increasingly use during vendor research — a practice known as generative engine optimization (GEO) or answer engine optimization (AEO).
In practice: Schema.org structured data (Service, Article, and FAQPage markup, visible in this page’s source) so crawlers can parse entities and Q&A pairs directly; an llms.txt file at the site root summarizing key pages for AI systems, the same way robots.txthas long guided search crawlers; explicit crawler access for GPTBot, PerplexityBot, and ClaudeBot; and answer-first writing — the direct answer to each heading’s question in the first sentence or two, before supporting detail — throughout this page and the linked guides.
Frequently Asked Questions About the Market Intelligence Copilot
What is an AI knowledge base built on proprietary research?
An AI knowledge base built on proprietary research is a conversational system that answers natural-language questions using only an organization's own newsletters, reports, and structured datasets — never the open internet or unsupported model knowledge. Every answer ships with a citation back to the source material it was built from.
How is this different from a generic AI chatbot?
A generic AI chatbot draws on broad internet training data and can produce confident, unsupported, or fabricated answers. A grounded Market Intelligence Copilot answers only from client-provided newsletters and structured datasets, returns an honest insufficient-evidence response when no supporting material exists, and cites the specific source behind every material claim.
What data does the Copilot need to get started?
Two proprietary knowledge assets: a library of newsletters or written research (HTML or text-based), and structured market data such as spend, share, or forecast figures in spreadsheet or database form. Neither needs to be perfectly organized before the first conversation — ingestion, schema design, and cleanup are part of the delivery process.
How much does a project like this cost?
A comparable fixed-scope build — architecture through production deployment and handover — has been delivered for $83,360 across 13 weeks with a five-person team. Optional MCP Connectors, which let external tools and AI assistants query the same grounded intelligence, add $10,000.
How long does it take to launch a Market Intelligence Copilot?
A comparable build runs 13 weeks across six stages: discovery and architecture, data ingestion, conversational intelligence and grounded answering, application development, MCP server integration, and end-to-end validation and deployment.
Can analysts still browse the raw newsletters and datasets directly?
Consumers — analysts and strategy professionals — interact through chat only: asking questions, reading cited answers, and reopening past conversations. They cannot browse raw datasets or newsletters directly. Administrators manage uploads, ingestion status, and user accounts, but cannot bypass processing or alter the answer-grounding rules.
Can the Copilot be embedded outside the chat interface?
Yes, through an optional MCP-compatible server. Approved external apps, websites, and AI assistants can submit authenticated questions and receive the same grounded, cited answers as the native chat interface — without ever exposing raw databases, embeddings, or internal prompts.
Does this replace our analysts or our forecasting process?
No. The Copilot does not replace analysts, newsletters, or forecasting methodology — it makes that existing work far easier to find, compare, and apply. Forward-looking answers still use only the client's own forecasts and expectations, never an independently generated model forecast.
How do you prevent the AI from making things up?
Through four guardrails enforced at the architecture level, not just by policy: numerical claims draw from structured data, interpretive claims rely on newsletter evidence, forecasts stay client-sourced, and unsupported questions return an honest insufficient-evidence response instead of a guess. Every material claim carries a traceable citation.
Who owns the resulting system and data?
The client's newsletters and structured data remain the client's own proprietary assets throughout. Delivery is structured so the client can own the resulting architecture, code, and operating knowledge according to the engagement agreement — the system is not a dependency on a black box only the vendor can maintain.
Related Ways to Explore This
Go Deeper on Grounded Market Intelligence
Supporting articles, guides, and tools published alongside this page. New pieces are added as they go live.
Turn Your Research Library Into a Grounded, Cited Copilot
Tell us what proprietary content and data you’re sitting on, how your analysts use it today, and what a grounded answer engine would need to cover. We’ll help define the right path — a scoping conversation, a sample-content walkthrough, or a full production build.