KORE is an intelligent memory layer that captures institutional knowledge, compounds it with every interaction, and delivers expert-quality recommendations at scale — automatically.
Product overview — 3 minutes
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Every senior engineer who leaves takes institutional knowledge with them. Every new inquiry starts from scratch. Every recommendation depends on who happens to be available. The PDQ process isn't broken — it's a symptom of an organisation that has never had a way to make expertise portable.
PDQs arrive with missing parameters. Technical teams spend days following up instead of solving. Every delay erodes customer confidence.
When Alex is unavailable, the organisation stalls. There is no codified decision logic to fall back on. Every case that requires judgment creates a bottleneck.
"Full capacity in summer" means something specific in membrane engineering. Translating customer language into technical thresholds requires experience that can't be looked up.
Engineers research incomplete datasheets and rely on memory to identify MH alternatives. Inconsistent, slow, and impossible to scale as competitor product ranges grow.
"The solution isn't faster search. It's a system that learns to think like their best engineer — and gets better every time it's used."
Ben's anxiety isn't speed — it's confidence under uncertainty. When he sends a recommendation, he's putting his professional judgment on the line. A system that gives him a shortlist he can't defend won't get used.
What Ben needs is to walk into every customer conversation already prepared. KORE delivers this automatically, before he even opens the inquiry.
What disappears: manual parameter extraction, catalogue searching, cross-referencing past cases, formatting outputs, remembering compliance requirements.
Today: Receives PDQ via email. 2 hours to extract parameters, search catalogue, chase Alex for edge cases. Output inconsistent.
With KORE: Opens inquiry. Parameters already extracted and confidence-scored. Shortlist generated. Historical cases surfaced. Compliance checked. Customer summary drafted.
Ben's job: Read in 90 seconds. Apply judgment. Send. The system did the hour. Ben did the judgment.
Alex is simultaneously the most valuable person in the process and its biggest bottleneck. He's been asked to document his knowledge before. It never worked — because it required him to stop working and start writing.
KORE captures Alex's knowledge through his normal workflow. Every override, every case he touches, every one-sentence rationale he gives is absorbed into the memory layer automatically.
Result: His accumulated knowledge handles 80% of cases. Only the 20% that genuinely need him reach him — as structured decisions, not open-ended reviews.
Today: Reviews all complex cases. Bottleneck for every edge case. Institutional knowledge trapped in his head, not in the system.
With KORE: Only genuinely novel cases reach him. Presented as structured one-sentence decisions with full context. Override rationale captured automatically.
Over time: His past decisions are in the system. His impact multiplies while his cognitive load drops.
The customer knows their operational problem. They don't know membrane science. Asking them to complete a 40-field PDQ presupposes a translation capability they don't have — which is why inputs are incomplete and follow-up cycles are long.
KORE replaces the form with a conversation. The customer describes their challenge in plain language. The system builds the technical parameter picture invisibly, one clarifying question at a time.
Output: A recommendation document clear enough to share internally with procurement — without needing MH on the call to explain it.
Today: Receives 40-field PDQ. Half the fields are unclear. Guesses at answers. Waits days for follow-up from Ben.
With KORE: Describes challenge in plain language. System asks one clarifying question at a time. Parameters built invisibly.
Result: Receives a clean recommendation they can take to procurement without needing MH present to translate.
Extract + score
Flag + follow up
Rank + explain
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Equivalency map
Processes incoming inquiries in any format — PDQ, email, PDF, free text. Runs a two-pass extraction: first pulling every parameter mentioned explicitly or implied by context, then scoring each by confidence. A customer who writes "our plant operates at full capacity during summer months" has communicated a duty cycle. The agent knows this. Missing critical parameters trigger targeted follow-up questions — specific questions whose answers resolve the highest-uncertainty parameters first.
The architecture is event-driven because a PDQ arriving is not a single transaction — it triggers five parallel operations. Request-response would serialise what KORE runs concurrently. Hover any component to see its role and the reasoning behind the choice.
No tool chosen fashionably. Every component earns its place by enabling a specific capability the simpler alternative couldn't provide. All open-source. All self-hostable. No proprietary data leaves controlled infrastructure.
Every validated recommendation, every expert override, every post-deployment outcome is absorbed into a persistent memory layer. The system at month 12 is a different class of product from the one at month 1. Competing tools start from the same baseline every time. KORE builds a moat that widens with every use.
Episodic memory for precedent. Knowledge graph for relational reasoning. Constraint-satisfaction for technical correctness. No single mechanism achieves all three simultaneously. Combining them produces recommendations that are precedent-grounded, relationally aware, and technically correct.
Every knowledge management initiative fails at the same point: it requires experts to stop working and start writing. KORE captures Alex's knowledge through his normal workflow — every override, every one-sentence rationale — without asking him to do anything differently. The knowledge base fills itself.
Competitor tools rely on permission filters — a misconfiguration exposes proprietary data under time pressure. KORE's internal/external boundary is enforced at the data layer. Proprietary specifications are structurally inaccessible to external outputs, not hidden behind a toggle that can be forgotten.
Institutional memory agent for US power grid operators. Fed 600 real incident records spanning 2019–2026. When a new fault arrives, GridTrace doesn't say "this happened before" — it says "here is the specific fix recommended after this incident that was never implemented, which may be why it's happening again." Session 50 is measurably faster and more accurate than session 1. This compounding memory logic is exactly what KORE applies to membrane product selection.
▶ Watch demoA DevOps-native agent giving every codebase a permanent, queryable institutional memory. Intercepts events across the software development lifecycle, captures reasoning at the moment of decision, surfaces it precisely when a developer needs it. The problem Cassandra solves — decisions made, context lost, next person starts blind — is structurally identical to the problem KORE solves for MANN+HUMMEL.
▶ Watch demoNo fine-tuning at launch. The system uses foundation models with structured prompting rather than domain-specific fine-tuned models. Fine-tuning would improve extraction quality significantly — but requires substantial labelled training data that doesn't exist yet. After 12 months of operation, accumulated validated cases become the fine-tuning dataset. Fine-tuning is the next evolution, not the starting point. Starting with fine-tuning would mean building on a foundation before the data exists to justify it.
Selection unlocks prototype development through the June–August evaluation window. The architecture is defined. The technology is chosen and justified. The risks are mapped with specific mitigations. The prior builds — GridTrace and Cassandra — demonstrate that the core capability works in production, in harder domains, under real conditions. What's needed: access to MANN+HUMMEL's product catalogue data and six months to demonstrate what a compounding memory layer looks like when pointed at the right problem.