Certo is applying rag (retrieval-augmented generation) to enterprise saas, representing a seed vertical AI play with core generative AI integration.
As agentic architectures emerge as the dominant build pattern, Certo is positioned to benefit from enterprise demand for autonomous workflow solutions. The timing aligns with broader market readiness for AI systems that can execute multi-step tasks without human intervention.
Certo is an agentic AI company that offers a product compliance platform for regulatory management.
A codified library of regulatory rules implemented as 'AI Compliance Agents' that (1) extract structured facts from unstructured supplier/regulatory documents, (2) execute explainable, citation‑backed checks across products and markets, and (3) auto‑generate regulator‑grade dossiers — combined with continuous live regulatory feeds and automatic rechecks.
Certo describes ingesting documents, extracting structured facts, continuously indexing regulation sources, and producing checks and reports that cite exact source pages. Those are strong signals of a retrieval layer (document/KB ingestion, search/embedding/index) feeding generation and validation outputs.
Accelerates enterprise AI adoption by providing audit trails and source attribution.
The product is explicitly framed as autonomous 'AI Compliance Agents' that orchestrate multi-step workflows (monitoring, rechecks, alerts, generating dossiers). That points to agentic systems with tool use (ingest/search/generate/check) and orchestration logic for continuous operation and alerts.
Full workflow automation across legal, finance, and operations. Creates new category of "AI employees" that handle complex multi-step tasks.
Language-to-executable artifacts is implied by 'regulatory rule becomes a programmable agent' and by converting unstructured regulations and supplier texts into structured validations and checks. This suggests translation of text rules into executable rules/logic or policy code.
Emerging pattern with potential to unlock new application categories.
The product claims to normalize and unify entities (ingredients, suppliers, product <-> regulation mappings), track versions and relationships, and surface cross-entity impacts — consistent with an entity-centric graph/knowledge-base approach (possibly with RBAC and audit features).
Emerging pattern with potential to unlock new application categories.
Agent-oriented, event-driven orchestration: discrete compliance agents continuously run against a product portfolio and regulatory corpus; events (reg feed update, product change) trigger rechecks and downstream actions such as alerts, supplier follow-ups, or doc generation.
Spent years at Roland Berger advising FMCG biggest players on strategy; observed how regulatory complexity slows launches.
Previously: Roland Berger
Engineer specialized in AI and automation; built data-driven products used for over 1,000 businesses.
Strong alignment: Bastien's regulatory and FMCG strategy background complements Jean's AI/automation technical leadership to build a platform that codifies regulatory rules and automates checks for large consumer goods brands.
partnership led
Target: enterprise
custom
field sales
• references to 'global brands' and focus on FMCG markets
• claims of enterprise-grade, regulator-backed outputs
Automated, auditable regulatory compliance checks across product materials, labeling, and marketing claims, with support documents and continuous monitoring
Creating and maintaining an up-to-date, globally-scoped regulatory rulebase is labor-intensive and defensible. When paired with automation, it becomes a significant competitive advantage that is hard to replicate purely with generic LLMs.
Certo operates in a competitive landscape that includes Assent Compliance, Selerant (and other PLM + regulatory modules), Enhesa / Sphera (regulatory intelligence & EHS).
Differentiation: Certo emphasizes agentic AI that codifies regulatory rules into continuously-running checks, automatic rechecks when regs or products change, and generation of regulator‑grade dossiers with exact source citations; Assent is broader supply-chain/GRC focused and less centered on automated, citation-backed AI agents and document generation like PIF/CPSR.
Differentiation: Selerant is PLM-first; Certo positions itself as an AI-first compliance agent layer that extracts facts from documents, runs explainable, auditable checks and auto‑generates regulator documents — prioritizing automated regulatory reasoning and continuous monitoring rather than PLM data governance as the core product.
Differentiation: These firms provide regulatory content/consulting and alerts; Certo claims to operationalize that intelligence into executable agents that automatically validate products, produce audit trails and generate submission‑ready documents, plus human‑in‑the‑loop workflows tailored for product launches.
They present 'AI Compliance Agents' — not just an extraction model but a productized orchestration of rules, workflows and triggers. This implies a DSL or rule-engine that codifies regulatory logic into modular, runnable 'agents' (likely separate services/workflows) rather than a single monolithic classifier.
Exact source citations and 'exact source page references' indicate a high-precision document pipeline: OCR/layout parsing, page/offset indexing and provenance mapping that can tie every extracted fact back to a specific page and location in an official PDF. That is non-trivial and requires more than plain NLP embeddings.
Automatic rechecks and 'Impact Preview' point to a dependency graph linking: products ↔ raw materials ↔ documents ↔ regulations ↔ checks. They must maintain incremental computation and event-driven recomputation so a single regulatory update can efficiently re-evaluate only affected checks.
Explainable formula checks suggest a hybrid numeric/symbolic validation layer: stoichiometric or mass-balance calculations, threshold logic and rule-based reasoning layered atop extracted ingredient lists (INCI/CAS). They are combining domain-specific deterministic logic with ML extraction.
Unifying materials by INCI/CAS and supplier implies advanced entity resolution and canonicalization across noisy supplier data, multi-lingual labels and synonyms. Maintaining a canonical ingredient graph is a hidden, high-effort capability.
Certo's execution will test whether rag (retrieval-augmented generation) can deliver sustainable competitive advantage in enterprise saas. A successful outcome would validate the vertical AI thesis and likely trigger increased investment in similar plays. Incumbents in enterprise saas should monitor closely for early signs of customer adoption.
“We build AI Compliance Agents that turn ever-changing regulations, supplier documents and formula rules into instant, auditable checks”
“Our AI agents surface issues early, but your regulatory experts maintain full control over final determinations”
“Transparent, auditable AI + human-in-the-loop review where it matters”
“Every compliance check comes with complete audit trails, legal citations, and step-by-step reasoning”
“Our platform generates regulator-grade reports with exact source page references”
“Regulation-as-programmable-agent: explicit framing of each regulatory rule as a programmable, auditable agent that continuously runs against a product portfolio.”
