NEIP is the National Education Intelligence Platform: a system that turns school-level records into national education intelligence for Nigerian government ministries and agencies, covering learning outcomes, curriculum completion and enrolment trends.

Where does NEIP's data come from?

From schools themselves. A school explicitly grants access, and its records flow into the national picture through a reviewed data exchange — schools are never enrolled without consent.

Access is restricted to accredited government ministries and agencies. Every NEIP account is tied to an agency role, and the dashboard is not reachable without one.

Federal Republic of Nigeria · Education Intelligence

Nigeria manages what it cannot see. NEIP makes learning visible — and acts on it.

The National Education Intelligence Platform gives ministries a live view of learning across states, LGAs and wards — then turns each gap into a costed intervention and measures whether it worked. Built on the records schools already keep, shared only with their consent.

Request a government demo Agency sign in

Access is restricted to accredited government ministries and agencies.

Status: NEIP is developed and operated by MirrorMingo for deployment by accredited Nigerian education authorities. It is not an official Federal Government of Nigeria service unless and until it is formally commissioned by a sponsoring ministry or agency.

The problem

The feedback loop is years long

Years, not weeks

A ministry learns that a cohort failed when WAEC or JAMB results land — long after the class it could have helped has moved on.

Once a year

The Annual School Census is the primary instrument. It is collected on paper, keyed in months later, and cannot answer a question asked this term.

Enrolment ≠ learning

Existing systems count children, classrooms and teachers. Almost nothing continuously measures whether learning is actually happening.

Every one of those is a measurement problem, not a willingness problem. NEIP's premise is simple: if a ministry can see learning weekly instead of annually, it can act while the children it is trying to help are still in the class.

Intelligence views

Federal → State → LGA → Ward → School

Federal

FME, UBEC, NERDC, TRCN, NEMIS, exam bodies

One national picture: learning health, curriculum delivery, exam readiness and the ranked actions to take — with the evidence behind each one.

State

State Ministries of Education, SUBEBs

The same intelligence scoped to one state, and hard-scoped: a state account cannot see another state's data at all.

LGA & Ward

LGEAs, ward supervision

Drill from a state to its LGAs and wards — all 774 LGAs and 8,798 wards — to the schools that need supervision this week.

School

Ministry officers, school leadership

One school's record: registration and official code, census history, submitted returns, interventions targeting it, and its live learning signal.

Sample dashboards

What an official actually sees

Illustrative sample — anonymised placeholder figures, not Nigeria's measured statistics

Learning health by state

State A

78%

State B

64%

State C

51%

State D

38%

In the live console this is an interactive map of all 36 states and the FCT. Clicking a state re-scopes every figure on the page to it.

National learning gaps

Algebra

58%

Mathematics · 12,400 learners affected · recovery cost estimated

Comprehension

44%

English · 9,150 learners affected · recovery cost estimated

Photosynthesis

39%

Basic Science · 6,020 learners affected · recovery cost estimated

Each gap carries the schools and states affected, teachers needed, and a planning-estimate recovery cost — and can be turned into a campaign in one click.

Intervention workflow

A closed loop, not a dashboard

Detect

The platform surfaces a real gap: a topic failing in specific classes, in specific schools, in a named LGA.

Decide

It proposes a costed intervention — what to send, expected improvement, timeline — and an officer approves it.

Deliver

Activation provisions the work: teachers receive assignments, and the school's own software is notified automatically.

Measure

Daily snapshots show whether the intervention actually moved learning — before and after, on the record.

The same pipe that reports the problem delivers the fix and measures the result. That loop — not the charts — is what separates intelligence from reporting.

How it is built

A working system, not a slide

Data-exchange architecture

School records

Kept by the school / its software

Consent grant

One-time code · no passwords shared · revocable

Ingestion

Identifiable learner fields dropped in code

Aggregate data plane

Jurisdiction-scoped · audited · DSA-gated

Agency console

Accredited officers only · 2FA

Intelligence flows one way; interventions flow back to the schools through the same pipe. The most sensitive data — identifiable children — is never collected, so it cannot leak.

A sample intervention lifecycle

1
Detect. Photosynthesis mastery in JSS2 Basic Science falls below threshold across a named LGA's schools.
2
Decide. The platform proposes a targeted practice campaign, estimates the movement and cost as a range, and an officer approves it.
3
Deliver. Assignments reach the affected classes and each school's own software is notified automatically.
4
Measure. Daily snapshots show mastery before and after — the result is recorded against the intervention.

Illustrative example — not a real cohort.

Deployment readiness

Live
Working platform. The agency console and data-exchange pipeline run today on the shared MirrorMingo API — this is a deployed system, not a mockup.
On request
Evaluation sandbox. An isolated sandbox with synthetic data can be provisioned for a ministry to trial against its own workflows.
Ready
Pilot deployment. Designed to start with a 2-LGA baseline pilot — register schools, invite teachers, run baseline diagnostics — inside one school week.
Planned
Independent security review. A third-party security assessment is scheduled ahead of any production deployment holding a jurisdiction's data.
Available
Data-sharing instruments. Template data-sharing and data-processing agreements are ready for a ministry's legal team to review.

Data governance

Built for the questions your DPO will ask

No identifiable learner records

NEIP holds aggregates. Named-child fields are rejected by the ingestion pipeline itself, not by policy alone.

Consent before data moves

A school or its software provider grants access with a one-time code. No passwords are shared, and a grant is revocable at any time.

Data-sharing agreements gate the data

Sensitive datasets stay held until the agency's instrument is signed and in force — enforced in code.

Every action is audited

Grants, approvals, interventions, submissions and queries are all recorded against the officer who made them.

Jurisdiction is enforced

Access is scoped to a jurisdiction on the server. A state officer cannot reach another state's data by any route.

NDPA-aligned by design

Aggregate-by-default, purpose-limited, with a documented retention position for each agreement.

Deployment

Runs on the government's terms

Government-owned tenant

NEIP runs as a dedicated government instance with its own accounts, audit log and data plane — separate from the consumer product.

Deploys to your infrastructure

The platform is a container plus PostgreSQL. It can run on Galaxy Backbone, an NITDA-accredited Nigerian data centre, or the ministry's preferred cloud.

Data stays where you require

In-country hosting is supported for jurisdictions that mandate data residency.

Integrates, does not replace

NEMIS/D-NEMIS remains the registry of record. NEIP consumes official registries and adds the intelligence and intervention layer on top.

Who owns, hosts, controls and operates it

The tenant is government-owned; MirrorMingo operates the software. These roles are distinct and stated in every deployment agreement.

Responsibility	Who holds it	Detail
Owner of the tenant & its data	The deploying government agency	Each NEIP deployment is a dedicated government instance. The agency owns the tenant, its configuration and all data held in it.
Data controller	The deploying government agency	The agency determines the purposes and means of processing for its jurisdiction's data and is the controller under the NDPA.
Data processor & software operator	MirrorMingo	MirrorMingo builds, maintains and operates the software and processes data only on the agency's documented instructions under a data-processing agreement.
Hosting	The agency's chosen environment	The tenant can run on Galaxy Backbone, an NITDA-accredited Nigerian data centre, the ministry's cloud, or MirrorMingo-managed hosting — the agency chooses, and in-country residency is supported.
Encryption keys & administrator access	Governed by the deployment agreement	For agency-hosted deployments the agency controls keys and top-level administrator access. For MirrorMingo-managed hosting, key custody and break-glass access are defined and logged in the agreement.

Full detail is in the Privacy Policy and Terms.

Government KPIs

What NEIP reports to a minister

National Learning Score

Measured mastery, weekly — not an annual proxy.

Learners Requiring Action

Named cohorts by state, LGA and topic.

Curriculum Delivery Index

What NERDC prescribes vs what was taught, practised and mastered.

Teaching Quality Index

Support signals aggregated — never named-teacher rankings.

Exam Readiness

WAEC, NECO, JAMB, BECE and NCEE cohorts banded before the sitting.

Data Readiness

How much of the sector is actually reporting — measured, not assumed.

Every indicator is computed from live records and carries the evidence behind it. Where the data does not yet exist, the platform says so rather than showing a confident zero.

Methodology

How the headline numbers are computed

Scores, indices and cost estimates are decision-support, not statements of fact. Each is defined below with its inputs, method, data threshold, uncertainty, bias safeguards and how it is validated. A human officer approves every recommendation — the platform never acts on its own.

National Learning Score

Inputs: Aggregated mastery signals from connected schools — attempts, completions and assessed outcomes against curriculum objectives.
Method: A weighted mastery rate per objective, rolled up to class, school, LGA, ward and state, then to a 0–100 index. Recent evidence is weighted above stale evidence.
Minimum data: A cohort is only scored once it clears a minimum sample of assessed learners and objectives; below it, the score is withheld, not estimated.
Confidence: Each score carries a coverage figure and is banded, not presented as a false-precision point value where data is thin.
Bias safeguards: Reported at cohort level only; low-coverage cohorts are flagged so a well-instrumented school is not mistaken for a high-performing one.
Validation: Backtested against subsequent exam-cohort outcomes (WAEC/NECO/JAMB/BECE/NCEE) where available, and recalibrated.

Curriculum Delivery Index

Inputs: What NERDC prescribes for the term versus what was taught, practised and mastered, from connected records.
Method: Share of prescribed objectives with evidence of delivery and practice, weighted by depth of evidence.
Minimum data: Computed only where a scheme of work and delivery signals both exist for the period.
Confidence: Missing signal is shown as 'not yet reported', never as zero delivery.
Bias safeguards: Distinguishes 'not taught' from 'not reported' so under-instrumented schools are not penalised as under-delivering.
Validation: Cross-checked against submitted returns and, where present, supervision reports.

Teaching Quality Index

Inputs: Aggregated support signals — practice coverage, feedback loops and learning movement — never individual teacher surveillance.
Method: A composite of aggregated support indicators at school and cluster level.
Minimum data: Only surfaced above a cohort size that prevents any individual teacher being singled out.
Confidence: Presented as a support signal for where to help, banded, with coverage shown.
Bias safeguards: By design it never produces named-teacher rankings; it points at where support is needed, not who to blame.
Validation: Reviewed against subsequent learning movement in the same cohorts.

Projected improvement & recovery cost

Inputs: The measured gap, the affected cohort size, the proposed intervention, and unit-cost assumptions the agency sets.
Method: A planning estimate: expected movement from comparable past interventions × affected learners, costed against agency-set unit rates.
Minimum data: Shown as a range, only where the gap and cohort are above the minimum sample; otherwise marked indicative.
Confidence: Always a range with stated assumptions — a decision-support estimate, never a guaranteed outcome or a committed budget.
Bias safeguards: Assumptions are explicit and agency-editable, so estimates are not a black box.
Validation: Every intervention's actual before/after result is recorded, closing the loop and improving future estimates.

Recommendations can be overridden or rejected by the authorised officer at every step; approval authority follows the agency's own governance, and every decision is audited against the officer who made it.

Next step

See it against your own jurisdiction

We will walk your team through the national picture, the intervention loop, and the data-governance architecture — including the data-sharing instrument your legal team will ask for before any data moves.

What is NEIP?: NEIP is the National Education Intelligence Platform: a system that turns school-level records into national education intelligence for Nigerian government ministries and agencies, covering learning outcomes, curriculum completion and enrolment trends.
Where does NEIP's data come from?: From schools themselves. A school explicitly grants access, and its records flow into the national picture through a reviewed data exchange — schools are never enrolled without consent.
Who can access NEIP?: Access is restricted to accredited government ministries and agencies. Every NEIP account is tied to an agency role, and the dashboard is not reachable without one.