Screenshot of the Research measurement drift investigation lab interactive demo
Screenshot of the interactive demo, on sample data

Research measurement drift investigation lab

Identify measurement issues before downstream analysis expands.

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For
Scientific facility quality teams
Solves
Calibration-related drift is confused with real experimental effects.
Delivers
Scientist-reviewed drift evidence report
Built in
about 4 weeks of creation time, MVP in 4 days
Investment
$23,500 for the MVP, $50,000 for the full product
Run it
Inside your business, or as part of your offer to clients
01

What it does

Identify measurement issues before downstream analysis expands.

  1. Detect contextual drift candidates.
  2. Compare reference behavior.
  3. Prepare technician investigations.
  4. Compare the reviewed result with the recorded baseline and value assumptions.
  5. Capture corrections and named-owner approval before consequential use.
  6. Export a versioned scientist-reviewed drift evidence report with source references and unresolved questions.

What goes in, what comes out

What the customer puts in
  • Authorized instrument logs
  • Expert-confirmed reference measurements

AI drafts, people review. Evidence-backed analysis and reporting workspace.

What the customer gets
  • Scientist-reviewed drift evidence report
02

How it works

The workflow

  1. In
    Start with

    Authorized instrument logs and expert-confirmed reference measurements

  2. 1

    Confirm the buyer's problem and scope

  3. 2

    Collect authorized instrument logs and expert-confirmed reference measurements

  4. 3

    Then follow this sequence: 1

  5. Out
    Finish with

    Scientist-reviewed drift evidence report

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Experts determine instrument validity; no automatic result correction. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data and definitions, Pattern investigation, Action and value review. Open with a compact overview and filters for the relevant period or segment. Let users drill from each theme or metric into underlying records. Keep source definitions and missing-data notes near the result. Use an action panel to assign investigations and record what was learned. Make the task-specific outcome scientist-reviewed drift evidence report visible beside its evidence, review state and value baseline.

Accounts and administration

Dataset permissions, field mappings, metric definitions, source drill-down, saved filters, reviewer annotations, recurring reports and action ownership. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Authorized datasets, papers, protocols, code and research records. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

03

How we build it

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

  1. 1

    Scoping call

    Day 1

    Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

  2. 2

    MVP

    4 days

    One buyer segment, one recurring use case; first modules: detect contextual drift candidates; compare reference behavior. Manual review in the loop. Built by our AI software factory.

  3. 3

    Paid pilot

    5 days

    Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

  4. 4

    Full product

    10 days

    Self-serve onboarding, billing, monitoring and the wider integration set.

  5. 5

    Run and improve

    Monthly

    We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

  1. Pick the riskiest assumption. Here: will scientific facility quality teams use it to solve "calibration-related drift is confused with real experimental effects"?
  2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
  3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Invalid rerun cost avoided minus investigation and calibration costs.
  4. Measure, then decide. Track invalid rerun cost avoided minus investigation and calibration costs; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: Experts determine instrument validity; no automatic result correction. Implement one approved input format, a bounded representative case set and the first two task modules: detect contextual drift candidates; compare reference behavior. Support the third module with operator review: prepare technician investigations. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around scientist-reviewed drift evidence report. Retain the explicit scope boundary: Experts determine instrument validity; no automatic result correction.

What the build depends on. Stable identifiers, consistent metric definitions, deterministic calculations, source lineage and representative review samples. Poor coverage must remain visible. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Experts determine instrument validity; no automatic result correction.

04

Investment

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

  1. Phase 1

    MVP

    One buyer segment, one recurring use case; first modules: detect contextual drift candidates; compare reference behavior. Manual review in the loop.

    $23,500 · about 4 days of creation time

  2. Phase 2

    Paid pilot

    Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

    $11,000 · about 5 days of creation time

  3. Phase 3

    Full product

    Self-serve onboarding, billing, monitoring and the wider integration set.

    $15,500 · about 10 days of creation time

Indicative total, MVP to full product$50,000about 4 weeks of creation time · start with the MVP from $23,500

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

StageHosting and infrastructureAI usageTotal per month
MVP and paid pilotabout 3 customers$30–$60$80–$160$110–$220
Full productabout 50 customers$110–$210$880–$1,750$990–$1,960
05

Run it or resell it

Internally

For your own team

Scientific facility quality teams run it inside the business: authorized instrument logs and expert-confirmed reference measurements in, scientist-reviewed drift evidence report out, reviewed by your people.

For your clients

As part of your offer

Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.

Your brand, or this one

Run it under your own brand, or start from this concept style.

  • primary#912748
  • accent#54c983
  • surface#f1e4e8
  • ink#22201e
Headings
Libre Baskerville
Text
IBM Plex Sans
Voice
Rigorous, transparent, cited
Selling it to your own clients: the go-to-market playbook

Pricing to test

Test USD 500-2,000 for an initial analysis of one bounded dataset. Offer USD 250-1,000 monthly for repeat reporting at agreed volume. Data cleanup and specialist analysis are separately priced. These are test ranges. Package the initial sale as one bounded scientist-reviewed drift evidence report. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Identify measurement issues before downstream analysis expands. Demonstrate a concrete scientist-reviewed drift evidence report using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Scientific facility quality teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample scientist-reviewed drift evidence report from a small authorized input set, with a transparent calculation of invalid rerun cost avoided minus investigation and calibration costs and no promised savings.

The first 30 days

  1. Week 1: interview five scientific facility quality teams and inspect a recent example of calibration-related drift is confused with real experimental effects.
  2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
  3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
  4. Week 4: measure invalid rerun cost avoided minus investigation and calibration costs, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Invalid rerun cost avoided minus investigation and calibration costs. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Invalid rerun cost avoided minus investigation and calibration costs; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs scientist-reviewed drift evidence report. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

Domain-specific definitions, trusted source mappings and a history connecting findings to actions and observed results. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for scientific facility quality teams. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Analysts, business intelligence dashboards, spreadsheets and general text summarization tools. Compare this product with the buyer's present method on invalid rerun cost avoided minus investigation and calibration costs. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Data preparation, reconciliation, classification, expert interpretation, customer-specific definitions and recurring reporting support. Additional initial validation requires representative authorized sample preparation, buyer interviews, qualified domain review and bounded validation of scientist-reviewed drift evidence report. Track cost per accepted output, including correction work, unsuccessful cases and support.

06

Safeguards

Preserve original data, methods, citations and research limitations. Use researcher review and document every substantive transformation. Experts determine instrument validity; no automatic result correction. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

Get this solution built

Built for you by our AI software factory, MVP in about 4 days. Tell us about your business and how you want to run it: inside your company, or as part of what you offer your clients. We reply within one working day.

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