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# Customer Intelligence & Segmentation **Segment your book. Predict what each customer does next. Price it in dollars.** Most tools sold as "customer intelligence" are descriptive segmentation with a better chart — they tell you what your customers already did. This one segments your book, forecasts what each customer will do next, and attaches a dollar figure with a confidence range to every forecast. Input: three columns — `customer_id, date, amount`. Available from Excel, Salesforce, HubSpot, Zoho or any accounting export today, no integration project required. Full results in under two minutes at any book size, on SOC-compliant infrastructure — and free while we're in early access. --- ## The two halves, and why both matter **Segmentation is the map. Intelligence is the decision.** **The segmentation layer** answers: who are my customers, grouped so I can act? It outputs A/B/C/D tiers, RFM behavioural cells, revenue concentration (Gini), and named buckets like Champions and At-Risk. It looks backward at what already happened. **The intelligence layer** answers: what will each one do next, and what's it worth? It outputs survival probability, predicted lifetime value, migration risk, value-at-risk, contact uplift, and total book exposure. It looks forward. Both refresh nightly. Segmentation alone produces a label. A label is a classification, not a decision — which is why CRM tier fields sit unused in every company that has them. The intelligence layer converts the label into an action and a number, and it's where this product actually lives. A concrete example of the difference. Segmentation says: *"Acme is an A-account."* Intelligence says: *"Acme is worth $60,000/yr, its purchase cadence has broken, lapse probability this quarter is 35%, that's ~$21,000 at risk ($16,000–$27,000, 90% CI), and a contact this week recovers an expected $14,000 of it."* Only one of those changes what someone does on Monday. --- # For the C-suite — CEO, CFO, Founder ### What's actually broken **The board question has no defensible answer.** "How much revenue is at risk this quarter?" is currently assembled by hand, a month stale, and nobody can reconstruct how it was calculated. **Concentration risk is invisible until it detonates.** If four accounts carry 60% of revenue and two have gone quiet, that fact is hiding inside a perfectly normal-looking dashboard. **You bought reporting and hoped for intelligence.** Dashboards describe the past. Enterprise-grade predictive scoring sits behind expensive editions plus a data-science setup — structurally priced away from a mid-market business with a clean CSV. **The alternative is headcount.** A data analyst runs $90,000–$130,000 a year and still cannot recompute the entire book every night. ### What you get **One number for the board, with its working attached.** Something like: *"$340,000 at risk across 23 neglected accounts. $520,000 of upgrade upside sitting in 41 B-accounts."* Produced by `book_exposure(book_id)`, and every dollar inside it traces to a named customer, a named model, and a training date. **Concentration health as a tracked metric.** A Gini coefficient on your revenue book alongside the A/B/C/D split — "we're too dependent on our top accounts" stops being a worry and becomes a number with a trend line. **Segments built on where customers are going, not where they've been.** Tiers are assigned on *predicted* lifetime value, not last year's invoices. Telling you who **will** be cream rather than who **was** is the entire product claim. **Numbers built to survive being wrong.** Every dollar figure ships as a range with the model's real confidence — *"~$42,000 at risk ($31,000–$54,000, 90% CI)"* — never a bare point estimate. This is deliberate. A manager shown a confident "$42,000", who watches the account churn anyway and then learns it was a rough guess, stops trusting every number you've ever shown them. A range plus a reason survives the miss. **Security and compliance your procurement team can sign off.** SOC-compliant infrastructure and controls across the MCP portfolio, so the security review isn't the thing that stalls a rollout. Transaction data is personal data under most privacy regimes — consent capture, retention policy and delete-on-request are built in for GDPR and DPDP-style requirements. **Answers in under two minutes, at any book size.** Not a batch job you queue and check tomorrow. In testing, a full book scores in under two minutes regardless of how many customers are in it — so "what's at risk right now" is a question you ask during the meeting, not after it. ### What it costs today **Nothing.** The full product — every tool, every segment, the complete intelligence layer, unlimited book size — is free while we're in early access. No card, no seat count, no feature gating. We'd rather have your book running nightly and your feedback on the numbers than a purchase order. Pricing arrives later; early users get told well in advance and won't be switched off mid-quarter. --- # For VPs — Sales, RevOps, Customer Success ### What's actually broken **Reps work the accounts they like**, not the accounts that move the number, and you have no defensible way to argue otherwise on a Monday morning. **Segments don't change behaviour.** "This is an A account" tells a rep nothing about what to do today. **The highest-leverage moment is invisible.** A customer migrating between segments — a B slipping toward C, a C climbing toward B — is where one call changes the outcome. You currently learn about it from the revenue chart, one quarter late. **You can't prove coverage works.** When finance asks whether rep outreach actually retains revenue, you have anecdotes. **QBR prep is spreadsheet archaeology**, rebuilt from scratch every quarter with slightly different logic. ### What you get **A daily queue ranked by money, not by segment.** This is the most important design decision in the product. Value of contact = customer lifetime value, multiplied by the difference between the probability they stay if contacted and the probability they stay if not. An A-account that stays loyal no matter what has near-zero uplift — it does not belong at the top of anyone's call list. A B-account a single call would visibly save has high uplift and belongs at the top. Segment-ranked queues systematically spend your team's best hours on accounts that were never going anywhere. `contact_uplift()` fixes that. **Segment migration, watched nightly.** `watch_migrations(book_id)` recomputes the whole book overnight and alerts on slips and jumps. That alert list is the morning queue, delivered into Teams or Slack — not another tab nobody opens. **Fast enough to use live.** A full rescore returns in under two minutes at any book size. A rep can pull a fresh read mid-pipeline-review, and you can re-run the book after a bad month instead of waiting for the next scheduled refresh. **Growth targeting, not just save-the-churn.** `rank_uplift_targets(tier)` answers "which of my B's are most likely to reach A if we touch them," using a migration model estimated from two transition matrices: accounts your reps touched, and accounts they didn't. That same comparison is your proof to finance that coverage works. **Explanations reps believe.** `explain_tier(customer_id)` returns the whole trace — the ABC baseline, the RFM cell, the survival probability, the migration risk, the model version, the training date. A rep who reads *"recency score collapsed from 5 to 2 in 60 days"* picks up the phone. A rep handed an opaque score from a black box ignores it, and the deployment dies quietly. **Two framings from one engine.** Urgency: *"Acme is worth $60,000/yr; 35% lapse risk this quarter, so $21,000 is at risk."* Growth: *"Invest an hour in Beta Ltd and unlock $18,000 of upside."* **Book and territory exposure on tap** for QBRs, headcount arguments and comp design — computed identically every quarter, so the trend is real rather than an artifact of whoever built this quarter's spreadsheet. ### The toolset **`score_customer_book(source)`** — the full segmented ledger across the book. **`watch_migrations(book_id)`** — who moved overnight, which becomes today's queue. **`contact_uplift(customer_id)`** — who to call first, ranked by dollars. **`rank_uplift_targets(tier)`** — which B's can be grown into A's. **`value_at_risk(customer_id)`** — what neglect costs on this account. **`book_exposure(book_id)`** — the number you report upward. **`explain_tier(customer_id)`** — why, auditable and versioned. **`predict_customer_value(customer_id, horizon)`** — expected future spend over a horizon. **`data_quality_report(source)`** — dedupe and gap detection before anything is scored. --- # For agent builders — developers, ISVs, RevOps consultants ### What's actually broken **An LLM cannot fit a survival model.** It can rank a CSV once, plausibly, and be wrong in a way nobody catches. Ask twice, get two answers. Ship that to a client and you own the outcome. **Rolling your own is a trap with non-obvious failure modes.** BG/NBD maximum-likelihood fitting breaks specifically: log-Gamma overflow, flat likelihood ridges, degenerate books that converge silently to nonsense. **No audit trail means no enterprise sale.** "The model said so" ends the conversation when a client asks why an account was segmented as C. **Most vendors ship a UI, not primitives.** You need composable tools to wire into an agent, not a dashboard to iframe. ### What you get **Nine deterministic MCP tools** over a standard interface. Same input, same model version, same answer — every time. Structured outputs designed for an agent to consume, not a human to squint at. **Hardened numerics, not a thin wrapper.** Built on the `lifetimes` OSS lineage and hardened for production: parameter bounds, multi-start optimization, convergence diagnostics, golden tests, and refuse-with-reason below data thresholds. When a book is too thin or too degenerate to model, you get an explicit refusal with a reason code — not a confident hallucination your client discovers in front of their board. **Everything versioned.** `model_version` and `trained_through_date` on every response, plus the full explanation trace. Reproducible and defensible six months later. **Cross-source by design.** CSV/XLSX first, so you can demo without an OAuth dance. Dataverse Web API, Salesforce REST/Bulk, HubSpot and Zoho read-only connectors as they mature. Native CRM scoring only ever scores its own CRM; real businesses keep data in an ERP and a CRM and Excel. **Confidence intervals as first-class output.** If you're building an agent that recommends actions, you need to know when the model is guessing — and so does whoever receives the recommendation. **Sub-two-minute response at any book size.** Model fitting is the expensive part of this problem class, and naive implementations degrade badly as the book grows. Ours returns a full scored book in under two minutes in testing, independent of customer count — so you can call it inside an agent loop instead of architecting around a long-running job, a polling endpoint and a callback. **SOC-compliant infrastructure and controls** across the MCP portfolio. If you're embedding this in something you sell, your customer's security questionnaire becomes a form you fill in rather than a project you run. ### The models underneath **Layer 1** — Pareto / ABC classification plus Gini concentration. Juran / GE, 1951. **Layer 2** — RFM behavioural scoring into named segments. Hughes, 1994. **Layer 3** — BG/NBD survival modelling: probability a customer is still active, and expected future purchases. Fader, Hardie and Lee, 2005. **Layer 4** — Gamma-Gamma spend model into discounted customer lifetime value. Fader and Hardie, 2013. **Layer 5** — Markov migration and uplift, estimated from touched versus untouched accounts. Pfeifer and Carraway, 2000. **Layer 6** — Assembly, explanation trace, and versioning. Layer 4 runs a published validity check most implementations skip: the correlation between purchase frequency and average order value must be near zero, and it is tested and reported, because Gamma-Gamma is invalid without it. **For consultants:** run multiple client books, white-label the reports, and repeat the engagement shape — score the book, present the exposure, run the queue, show the migration delta at the next review. Currently free, with no cap on the number of books. --- ## Where this sits in your stack Worth being precise, because the category name invites the wrong comparison. **It is not a CDP.** It doesn't unify identity, ingest event streams, or store profiles. It reads transactions and returns decisions. **It is not a CRM replacement.** Your CRM stays exactly where it is; this reads from it and writes decisions back into your team's workflow. **It is not a BI tool.** BI describes what happened and leaves interpretation to you. This forecasts what happens next and prices it. **It is not a campaign or marketing automation platform.** It tells you who to reach and what reaching them is worth. Sending is somebody else's job. The closest honest description: **a predictive segmentation and account-prioritisation engine that sits between your transaction data and your team's daily work.** --- ## What it won't do Honesty is part of the product, so, plainly: **It doesn't model pure-subscription books out of the box.** The survival models assume non-contractual purchasing where customers can lapse silently. Subscription and renewal books need the documented renewal-mode variant — the engine says so rather than quietly mis-modelling you. **It doesn't manufacture answers from thin data.** Below the data threshold you get an explicit refusal with a reason, not a fabricated number. **It doesn't give you a single confident figure with no range.** If that's what you want, this product will disappoint you on purpose. --- ## Start in five steps 1. Export `customer_id, date, amount` from wherever it lives. 2. Run `data_quality_report(source)` to dedupe and flag gaps before anything is scored. 3. Run `score_customer_book(source)` for the full segmented ledger in one call. 4. Run `book_exposure(book_id)` for the number that goes to the board. 5. Turn on `watch_migrations(book_id)` so the nightly recompute becomes tomorrow's queue. Steps 2 through 4 take under two minutes each, whatever the size of your book. Free while in early access, with no customer limit.

Drive real Android & iOS devices and web browsers from natural language for mobile + web QA. 145+ tools across device control, app management, automation sessions, browser automation, and flow recording / replay. Bearer-auth — get a token at robotactions.com → Profile → API Tokens.

# **RChilli MCP Hub** RChilli MCP Hub is a production-grade MCP server that exposes RChilli's full HR data intelligence platform as 17 AI-callable tools across 4 categories. Built on 15+ years of HR data intelligence, it is trusted by ATS vendors, HR technology platforms, staffing agencies, and enterprise recruiting teams worldwide. Every tool is read-only and returns a consistent, structured JSON response — no raw exceptions, no inconsistent formats. <br> --- <br> # **Tools — 17 Total** userkey and subuserid are injected automatically from your Bearer token — you never need to pass them manually. <br> --- <br> # **🔍 Resume & Job Description Parsing — 3 tools** <br> > ### **`extract_resume_data`** > > Extracts and converts resumes, CVs, and candidate documents into structured, searchable profiles with contact details, skills, experience, education, certifications, and taxonomy-enriched data for ATS, HCM, and AI recruiting workflows. When used on a careers page or application form, the same extraction call auto-fills every application field in under 10 seconds — documented to increase candidate conversion by up to 194%. Supports 40+ languages with English-normalized output for global intake, and runs in batch mode to process legacy databases or migration backlogs overnight at scale. Also supports resume reprocessing — re-running previously extracted resumes through the latest extraction logic and taxonomy version to bring older records up to current data quality, without requiring a new document from the candidate. Distinct from bulk import (first-time extraction of a new batch) and from talent data refresh (re-enrichment from a newer submitted resume). <br> > ### **`extract_resume_data_from_url`** > > Accepts a direct URL to a PDF, DOCX, or RTF file and returns the same normalized JSON profile as the Resume Data Extraction tool. Ideal for pipeline automation where resumes are stored in cloud storage, S3, or email attachments. Also supports the same auto-fill, multilingual, and batch-processing capabilities as the core extraction tool for URL-based intake sources. <br> > ### **`extract_job_data`** > > Extracts and converts job descriptions into structured hiring data including job title, required skills, preferred skills, responsibilities, experience, education, and taxonomy-normalized role requirements for recruitment automation and candidate matching. <br> --- <br> # **🧠 Skills & Job Taxonomy — 4 tools** <br> > ### **`lookup_skill`** > > Returns authoritative detail for a known skill including description, all aliases, related skills, proficiency levels, and O*NET/ESCO mappings. Use when you need the complete record rather than a ranked search. <br> > ### **`lookup_job_profile`** > > Returns authoritative detail for a known job profile including canonical title, SOC/O*NET code, job family, typical required and preferred skills, salary bands, and work context. <br> > ### **`autocomplete_skill`** > > Accepts a partial skill string (min 2 chars) and returns up to 10 ranked autocomplete suggestions with canonical names and categories. Prevents free-text entry errors and keeps skill data clean at point of entry. <br> > ### **`autocomplete_job_profile`** > > Accepts a partial job title string and returns ranked autocomplete suggestions with canonical titles and job families. Ensures job titles map to taxonomy profiles from the moment a recruiter starts typing. <br> --- <br> # **🛡️ Redaction, Documents & Utilities — 7 tools** <br> > ### **`redact_resume`** > > Redacts personally identifiable information from candidate profiles to support anonymized review, bias-aware screening, compliance workflows, and audit logs. Configurable redaction scope. Idempotent. <br> > ### **`reformat_resume_with_template`** > > RChilli's Resume Reformatting tool accepts any structured candidate profile and applies one of six branded templates (TM001–TM006) to produce a consistently formatted output document in PDF, DOCX, RTF, or HTML — ensuring every candidate is presented in a standardized, professional layout regardless of how their original resume was structured. Designed for staffing firms, recruitment agencies, and enterprise HR teams who need to control candidate presentation at scale, it eliminates manual reformatting effort and enforces brand consistency across all submissions. <br> > ### **`convert_document_format`** > > Accepts a document as base64 or URL and converts between PDF, DOCX, RTF, HTML, and plain text. Preserves formatting fidelity. Useful as a pre-processing step before data extraction on non-standard file types. <br> > ### **`tag_entities`** > > RChilli's Named Entity Recognition tool takes already-extracted HR text and annotates it by wrapping each recognized entity in a structured XML-style label inline — returning output such as `<job_title>Senior Data Engineer</job_title>`, `<skill>Python</skill>`, `<city>Austin</city>`, `<degree>Bachelor of Science</degree>`, and `<organization>Google</organization>` — covering 10+ HR-specific entity types including person name, state, country, date, and year. Unlike data extraction tools that produce separate field lists, tag_entities preserves the full original text structure with entities labeled in place, making the output immediately consumable by ATS field-mapping pipelines, candidate profile builders, and content annotation workflows without any offset calculation or post-processing. <br> > ### **`extract_contacts`** > > Identifies and structures names, emails, phone numbers, LinkedIn URLs, and addresses with field-level confidence scores from candidate records, emails, or documents. Safe for GDPR/CCPA workflows. <br> > ### **`geolocate`** > > Converts partial or informal location text into structured city, state, country, ISO codes, latitude, and longitude. Enables radius-based candidate and job search and supports workforce planning analytics. <br> > ### **`classify_job_zone`** > > RChilli's Job Zone Classification tool reads the job profile from a resume or job description and returns its O/*NET Job Zone — one of five standardized levels ranging from Zone 1 (little or no preparation required) through Zone 2 (some preparation), Zone 3 (medium preparation), Zone 4 (considerable preparation), to Zone 5 (extensive preparation required) — based on the education, experience, and training criteria defined by O/*NET. The returned Job Zone level enables downstream workflows such as candidate-to-role fit filtering, compensation benchmarking, over/under-qualification flagging, and job architecture standardization without any manual O/*NET lookup. <br> --- <br> # **🎯 Search & Matching — 3 tools** <br> > ### **`score_resume_against_jd`** > > Accepts one resume and one Job Description (no index required) and returns an overall match score, dimension scores, skill gap list, and natural-language explanation. Bias-controlled and audit-ready. <br> > ### **`find_matches_in_index`** > > Accepts a resume or Job Description as input and returns the top-N most similar documents from the indexed corpus ranked by semantic similarity. No index setup required for the input document. <br> > ### **`search_indexed_documents`** > > Accepts a query string and returns ranked document references from the tenant's pre-populated index. Supports Boolean and semantic search modes. Requires documents to be indexed before use.

Control Plane (controlplane.com): deploy and operate workloads across AWS, GCP, Azure, and more.

AppDeploy turns app ideas described in AI chat into live full-stack web applications

WhatsApp (Web + Business API), SMS, contacts, and call records via 2Chat's MCP server.

Your unified inbox — everything that reaches you, understood and actionable from your AI assistant.

Marketing analytics for local businesses — Instagram, ads, web traffic, SEO and reviews.

Connect your AI assistant to your Peec AI account to monitor and analyze your brand's visibility across AI search engines like ChatGPT, Perplexity, and Gemini. Ask questions about brand visibility, competitor comparisons, source citations, and trends: all in plain language, directly from your AI tools.

Connect AI agents to BoomTax for IRS information return filing. Query filings (1099, W-2, 1095, etc.), check e-file status and errors, look up payers, and get filing summaries across tax years. **Tools:** - Search and filter filings by tax year, form type, and status - Get filing details with payer info and e-file status - View e-file errors with IRS error codes and messages - Look up payers/issuers with filing counts - List all supported filing types and e-file availability

Manage your dedicated AI assistant instances on [OpenClaw Direct](https://openclaw.com) through natural language. Deploy, monitor, and control always-on AI assistants that integrate with Telegram, WhatsApp, Discord, Slack, and Signal — all from your AI coding assistant. Learn more about the [MCP integration](https://openclaw.com/openclaw-mcp-integration).

Visit https://brave.com/search/api/ for a free API key. Search the web, local businesses, images,…

An API for forecasting and multi-agent research. FutureSearch provides endpoints that use web research agents at scale, for higher accuracy than web search or single agent approaches alone can achieve. forecast runs a team of forecasters to predict future dates, numbers, and probabilities. multi_agent orchestrates multiple researchers to answer one question. agent_map runs one research agent over every row of a dataset, scaling to thousands of rows and agents.

Privacy-first web analytics, exposed to your AI agent as a first-class data source. The agent sees your traffic, referrers, geos, devices, live visitors, and custom events, and can reason across them. Ask what changed since the last deploy, why a campaign underperformed, which segment of signups actually activated, or have it build a conversion funnel and alert you when bounce rate spikes.

Access and interact with anime and manga data seamlessly. Retrieve detailed information about your…

An MCP server for deep research or task groups

Provide real-time and forecast weather information for locations in the United States using natura…

The best web search for your AI Agent

Look up DNS information for any domain to troubleshoot issues and gather insights. Get fast, relia…

Enable AI assistants to perform web searches using Perplexity's Sonar Pro.