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Industries · Government & Public Sector

AI for public services, with governance built in.

Public organizations need AI that answers accurately, keeps internal guidance internal, and stands up to public scrutiny. I've built a dual-endpoint assistant serving both citizens and field teams from one governed knowledge base, sized for 10,000+ queries a day.

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Where AI pays off

What I build for teams like yours.

  • 01

    Citizen information assistants

    Accurate answers about programs and services, grounded in official sources.

  • 02

    Field and staff copilots

    Operational guidance for staff, isolated from public-facing content.

  • 03

    Archive digitization

    Scanned records turned into searchable, structured data.

  • 04

    Publishing records safely

    Personal data anonymized before decisions or records are released.

What makes it hard

The constraints are the project.

The model is rarely the problem. These are the requirements that decide whether a system ships, and they are designed in from the first week.

  • Audience separation

    Namespace-isolated retrieval so restricted content never reaches the public.

  • Past vs. planned

    Temporal routing keeps delivered programs and future commitments distinct.

  • Peak traffic

    Capacity planned for surges around announcements and deadlines.

  • Transparency

    Cited answers and logs that make the system's behavior reviewable.

Proof

Relevant case studies.

civic-assistant · dual endpoint
10K+/day
“When does the youth scholarship program expand?”temporal router → planned

Publicns: public

The expansion is part of the next program cycle. Eligibility criteria and official announcements are listed on the program page.

2 public sources

Field teamns: field

Rollout guidance for coordinators: priority regions, required documents and the household briefing checklist.

3 field sources · restricted

one knowledge base · two namespacesno cross-audience leakage

Illustrative interface · client data never shown
01Civic Intelligence Assistant

High-Volume Public-Sector RAG Assistant

A dual-endpoint RAG assistant that serves the public and the field teams of a national political organization from one governed knowledge base. It is sized for 10,000+ queries a day, with temporal query routing and namespace-isolated retrieval.

Queries per day capacity
10K+
Audience-specific endpoints
2
Governed knowledge base
1
Read case study
anonymizer · triple-search
98% detection

Original

Le mineur Koffi A., domicilié à Cocody, rue des Jardins, représenté par sa mère Awa D.… K. Adjouaconfirme…

Anonymized

Le mineur [MINEUR 1], domicilié à [ADRESSE], représenté par sa mère [PARTIE B]… [MINEUR 1]confirme…

  • Pass 1Contextual entities (Claude)
  • Pass 2Patterns & identifiers
  • Pass 3Variants & later mentions

4 entities · 1 variant spelling caughtminor-protection rules applied

Illustrative interface · client data never shown
02Legal Anonymization Engine

Multi-Jurisdiction PII Anonymization Engine for Court Decisions

A compliance-grade anonymization engine for legal case decisions. A triple-search architecture finds personal data that single-pass detection misses, reaching 98% detection accuracy with dedicated safeguards for minors and vulnerable parties.

PII detection accuracy
98%
Independent search passes
3
Decisions anonymized before release
100%
Read case study
Enterprise Knowledge Management5 stages
  1. 01PDF
  2. 02Image
  3. 03Clean
  4. 04OCR
  5. 05Structure

Stage 1, PDF: Scanned document intake. Stage 2, Image: Page-to-image conversion. Stage 3, Clean: Preprocessing for OCR accuracy. Stage 4, OCR: Text extraction. Stage 5, Structure: Vector-ready JSON.

03Document Intelligence Pipeline

OCR-to-RAG Document Ingestion Pipeline

A five-stage pipeline that converts scanned PDFs into clean, structured, vector-ready JSON, opening up archives that search and RAG could not read before.

Processing stages
5
Unstructured to structured
Scan → JSON
Vector-store output
RAG-ready
Read case study

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FAQ

Government & Public Sector: common questions.

How do you keep internal guidance out of public answers?

Content is partitioned into namespaces per audience and each endpoint retrieves only from what it's entitled to. It's enforced in retrieval, not left to the prompt.

Can the assistant handle high traffic?

Yes. The civic assistant was sized for 10,000+ queries a day, with caching and model choices planned for peaks.

Can it work in local languages?

Multilingual retrieval and generation are supported; the pipeline is evaluated on real questions in each language before launch.

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