Overview · positioning
This isn't a dashboard. It's an agentic decision system grounded in a business ontology.
Reporting
What happened?
→
Analytics
Why did it happen?
→
Agentic
What should we do — and who owns it by when?
You are here
Section 1 · The difference
Management reporting vs. an agentic decision system
Management reporting
"Show me last quarter."
- DirectionRetrospective
- OutputA chart, a table, a PDF
- KnowledgeRows in a fact table
- AI roleOptional caption / nice-to-have
- AudienceOne report for everyone
- ActionImplied — left to the reader
- DrillFilter / pivot
- StateRefreshes nightly
VS
Agentic decision system
"What should we do, framed for the board?"
- DirectionForward-looking · recommends
- OutputImplication + evidence + actions w/ owner & date
- KnowledgeTyped ontology + linked KB graph
- AI rolePrimary reasoning agent · grounded
- AudienceSame KB, read through CEO / CFO / COO personas
- ActionExplicit · owner · by-when · ties to a decision
- DrillClick any evidence chip → back into the graph
- StateListens to news + signals · re-tags impact live
The litmus test
Ask the system: "What does PSA mean for Avery Dennison in the next 90 days? Cite specific evidence and recommend one action."
A dashboard can't answer that. An agent grounded in a business ontology can — because PSA isn't a string, it's an entity linked to a domain (MG.POLY), a value-chain stage (Inbound), a category (Materials), customers, KPIs and owners. The agent traverses those links, then a CEO-persona writes the answer.
Section 2 · The agent loop
How ontology + KB + taxonomy turn a question into a grounded answer
Each step below is a real component in the app. The grey boxes are knowledge artifacts (static, curated). The blue boxes are agent operations (live, per-question). The orange box is the LLM call. The arrows show how grounding flows in — that's what stops the model from hallucinating.
①
User question
"Frame the Walmart ramp for the next earnings call."
↓
②
Intent + entity extraction
Pulls Walmart, earnings call, Solutions Group out of the prompt
↓
⇲
③a
Ontology lookup
62 typed terms · finds RFID, atma.io, HVC; resolves RBIS = Solutions Group
Open ontology →
③c
Taxonomy / typing
Knows Walmart is a Customer, RFID is Tech, EVA is Finance — so the agent reasons at the right level
Open domains →
⇱
↓
④
Grounded context assembled
Definitions, current values, owners, related entities — packed into the prompt so the model can only cite real facts
↓
⑤
LLM reasons as a persona
CEO · CFO · COO · CHRO — same KB, different voice and decision lens
Meet the copilots →
↓
⑥
Structured agentic answer
Implication
Evidence (clickable chips → back into KB)
Actions · owner · by-when
Follow-up questions
↻
User drills any evidence chip back into step ③ — the system is a loop, not a pipeline. Drilling enriches the next question.
Section 3 · The grounding stack
Three knowledge layers that make AI act like an agent, not a chatbot
01
Taxonomy
The typing system
17 categories — Brand, Person, Materials, Tech, KPI, Regulation, …
Why it matters for agentic AI:
- Without it, the LLM treats "RFID" and "Walmart" as the same kind of string.
- With it, the agent knows to look up financial impact for a KPI, resolve owner for a Person, check regulation date for a Regulation.
02
Ontology
The vocabulary
62 typed terms with definitions, domain refs, and a "what this means for AVY" implication.
Why it matters for agentic AI:
- Stops jargon hallucination — the LLM quotes the curated definition rather than inventing one.
- Enables semantic search: ask "EU compliance terms" → ontology returns DPP, ESPR, Scope 1/2/3, even though none contain the word "EU".
03
Knowledge base / graph
The relationships
9 domains, sub-domains, customers, competitors, KPIs, value chain, journeys — all linked.
Why it matters for agentic AI:
- Lets the agent traverse: "Walmart" → Top Customer → SG segment → Francisco Melo → atma.io → DPP impact.
- Every answer can cite a path through the graph; every claim is back-traceable.
Taxonomy
types the data
+
Ontology
defines the terms
+
KB / graph
links the entities
=
Agentic AI
that can reason, cite, and act
Section 4 · What it unlocks
Six things only work because of the grounding stack
①
Acronym-aware answers
"What does PSA mean for AVY in the next 90 days?"
Ontology resolves PSA → Pressure-Sensitive Adhesive · domain
MG.POLY · category Materials. Agent cites that definition verbatim and recommends an action grounded in the real product portfolio.②
Persona-switching on one KB
"Frame the Walmart ramp for the next earnings call." — asked of CEO, then CFO, then COO.
Same facts, three lenses: CEO talks growth narrative, CFO talks revenue uplift & margin, COO talks rollout risk. No three databases — one ontology, three personas.
③
Semantic, not keyword
"Show me terms tied to EU compliance."
Returns DPP, ESPR, Scope 1/2/3, SBTi — none of which contain "EU". LLM ranks by meaning against the typed ontology, not a substring match.
④
Cross-entity comparison
"Compare Smartrac vs TexTrace on the differentiation lens."
Both resolve to acquired-brand entities under
SG.RFID / SG.EMBEL.TEX. Graph traversal pulls year, parent domain, and overlap — then the LLM scores the comparison.⑤
Action with ownership
"Recommend one action on HVC margin."
Agent returns: "Lift HVC mix from 40 → 45% in Wine & Spirits" — Owner: SVP & GM LPM · By: end of Q3. Owners come from the domain hierarchy, not invented.
⑥
Live signals tagged to entities
DDGS news feed → ontology auto-tag → cockpit ticker.
A headline about "Walmart RFID expansion" is auto-routed to Walmart (Customer), RFID (Tech), SG.RFID (Domain) — and surfaces on the relevant 360 page, not in a generic feed.
Section 5 · Where the value lands
Three pillars for Data & AI big bets — and how agentic adds value to each
Every agent in the registry must trace its value to one of these three P&L levers. If it doesn't move revenue, monetize data, or expand margin, it doesn't ship.
AD value chain → where each pillar lives
R&D · NPI
HVC labels · RFID inlays · CleanFlake · DPP substrates
Pillar 01
→
Make
MG coaters · slitters · SG inlay lines · 50+ plants
Pillar 03
→
Sell · Serve
Diageo · L'Oréal · Walmart · Inditex · Pfizer
Pillar 01
→
Connected items
atma.io · RFID · QR · DPP events
Pillar 02
Pillar 01 · Top line
Revenue Growth
NPI for HVC labels, RFID inlays & sustainable substrates · R&D acceleration
Brand brief
→
DDGS signal
→
Ontology match
→
NPV rank
→
Pilot launch
The lever — for AD
- Compress NPI from brief → pilot → launch on HVC wine & spirits, beauty, pharma labels
- De-risk R&D bets on RFID inlay generations, CleanFlake (PET-recyclable PSA), DPP-ready substrates
- Open whitespace from Walmart / Inditex / H&M apparel RFID into food retail, pharma serialization, beauty
How agentic adds value
- NPI Scout — surfaces unmet jobs from Diageo, L'Oréal, Pfizer in CRM + DDGS feed + win/loss notes
- R&D Triage — ranks pipeline weekly: RFID Gen-3 vs CleanFlake vs HVC PSA by NPV × strategic fit
- Voice-of-Customer — clusters tickets & call notes into themes ("liner waste", "fast-fashion velocity")
- Patent Watch — flags Smartrac / TexTrace / Checkpoint filings vs SG.RFID & MG.POLY whitespace
AD case in action
A tamper-evident HVC brief from Diageo triggers NPI Scout, which finds parallel asks from Pernod Ricard and LVMH Wines. R&D Triage lifts the project's NPV, Voice-of-Customer attaches live counterfeit incidents — pilot starts in 6 weeks instead of 6 months.
Pillar 02 · New revenue
Data Monetization
Turn AD's full data estate — connected items, materials science, plant & market signal — into products
Data source
→
Consent check
→
Aggregate
→
Narrative
→
Buyer API
AD data estate — the raw material
- Connected-item events · atma.io / RFID / QR / DPP — traceability, recall, brand-protection
- Materials & sustainability · substrate performance, CleanFlake recyclability, lifecycle & carbon data
- Plant & process · coater yield, scrap, OEE across 50+ plants — anonymized as industry benchmarks
- Market & pricing · quote / win-rate, channel signal, distributor patterns from CRM & ERP
Products that can ship from it
- Industry indices · Apparel Velocity · W&S Authentication · Pharma Cold-Chain · Food Traceability
- Sustainability & carbon dashboards (PSA labels, packaging) for brand sustainability buyers
- DPPaaS compliance subscription · apparel · footwear · EV battery · cosmetics
- Data-as-a-Service APIs for brands, retailers, regulators & insurers
How agentic adds value
- Data Product — assembles, governs & prices a product from any source (atma.io, materials lab, MES, CRM)
- Benchmark — anonymizes across customer · plant · SKU dimensions into sellable indices
- Contract & Consent — enforces purpose-of-use, brand-owner consent, residency & rev-share
- Insight-as-a-Service — delivers narrative answers from any data product to brand · retailer · regulator buyers
AD case in action
A sustainability buyer at L'Oréal asks for a packaging carbon-footprint benchmark across PSA label families. Contract & Consent validates AVY's right to publish the anonymized dataset, Benchmark aggregates lifecycle data from the materials lab + MG plants, Insight-as-a-Service delivers a narrative + API — a net-new sustainability subscription ships in days, no atma.io scan event involved.
Pillar 03 · Bottom line
EBITDA Margin Improvement
Coater yield · slitter throughput · drying-oven energy · SKU-tail profitability
MES sensor
→
Ontology resolve
→
Agent rec
→
Operator action
→
Telemetry back
The lever — for AD
- Coating yield in MG plants — first-pass quality on PSA-coated webs, edge & coating-defect scrap
- Throughput — coater line speed, slitter changeover between SKUs, SG inlay-line OEE
- Cost efficiency — drying-oven energy, freight on heavy rolls, liner inventory working capital
- Cost-driver analysis — 30k+ SKUs, customer-level profitability, plant-by-plant unit cost
How agentic adds value
- Yield Optimizer — recommends setpoint shifts (oven temp, web tension, coat weight) on Mentor OH Line 7 PSA runs
- OEE / Throughput — root-causes changeover downtime between W&S SKUs on slitter lines
- Cost-Driver — decomposes COGS by SKU × plant × customer; flags negative-margin tails in HVC variants
- Procurement & Energy — hedges paper, silicone & acrylic adhesive; reroutes freight against fuel signals
AD case in action
Yield Optimizer spots a 60% scrap spike on Mentor Line 7 during a W&S coating run. It cross-checks recipe v3.2 in the KB, simulates oven-temp + tension changes, and sends the shift supervisor a one-click fix — yield recovers in 2 shifts, ~$180k of monthly scrap avoided on that line alone.
SIPOC — how each pillar runs, end-to-end
Same lens, three pillars: Suppliers · Inputs · Process (the agents) · Outputs · Customers. Each row is what the agent fleet actually consumes and produces — not abstract, all AD-real.
Pillar 01 · Revenue Growth
NPI & R&D acceleration
S · Suppliers
Diageo · L'Oréal · Pfizer brand teams · Salesforce CRM · DDGS news feed · USPTO / EPO · Win-loss notes
I · Inputs
Brand brief · sell-through signal · competitor filings · support tickets · NPS verbatims
P · Process
NPI Scout
→
Voice-of-Customer
→
R&D Triage
→
Patent Watch
O · Outputs
Ranked NPI pipeline (NPV × fit) · pilot brief · IP-risk flag · prioritized feature set
C · Customers
R&D · Product Marketing · MG / SG GMs · Account teams · CGO
Pillar 02 · Data Monetization
atma.io as a product
S · Suppliers
atma.io platform · Brand owners (consent) · Retailers (sell-through) · EU regulators (DPP/ESPR)
I · Inputs
Serialized RFID / QR scan events · brand consent records · contract terms · residency rules
P · Process
Contract & Consent
→
Benchmark
→
Data Product
→
Insight-as-a-Service
O · Outputs
DPP-as-a-Service · Apparel Velocity Index · Authentication API · narrative insights
C · Customers
Brand owners · Retailers · Sustainability / ESG buyers · Regulators · CDO of partner co.
Pillar 03 · EBITDA Margin
Yield · throughput · cost
S · Suppliers
AVEVA Historian · Rockwell PLC · SAP CO / PP · BW · Coupa · Commodity / freight feeds
I · Inputs
Process tags (temp, tension, coat-wt) · downtime codes · COGS bridge · paper / silicone / adhesive prices
P · Process
Yield Optimizer
→
OEE / Throughput
→
Cost-Driver
→
Procurement & Energy
O · Outputs
Setpoint recommendation (oven temp · tension) · downtime root cause · negative-margin tail · hedge plan
C · Customers
Shift supervisors · Plant managers · COO · CFO · CPO
Agent collaboration — how the fleet hands off across pillars
Real value compounds when an insight in one pillar triggers an action in another. SCIKIQ's A2A bus lets agents post events on the shared ontology — any other agent subscribed to that entity reacts. Below are the live cross-pillar handoffs.
Revenue · Pillar 01
NPI Scout
R&D Triage
Voice-of-Customer
Patent Watch
Data $ · Pillar 02
Data Product
Benchmark
Contract & Consent
Insight-as-a-Service
EBITDA · Pillar 03
Yield Optimizer
OEE / Throughput
Cost-Driver
Procurement & Energy
Cross-pillar A2A handoffs (live in SCIKIQ)
Patent Watch
→
R&D Triage
Smartrac files a competing RFID inlay patent — Triage re-ranks SG.RFID Gen-3 ahead of CleanFlake this sprint.
Voice-of-Customer
→
Data Product
Three brands ask for "wine authentication" data — Data Product packages a W&S Authentication Index from atma.io.
Benchmark
→
NPI Scout
Apparel sell-through spikes in fast-fashion velocity — Scout opens a brief for higher-density RFID inlays.
Yield Optimizer
→
Cost-Driver
Mentor Line 7 scrap drops 18% — Cost-Driver re-bridges COGS and flags HVC unit margin uplift to CFO.
Cost-Driver
→
R&D Triage
Negative-margin tail in low-volume HVC variants — Triage moves SKU-rationalization R&D items up the queue.
Contract & Consent
→
Insight-as-a-Service
Brand consent & EU residency green-lit — IaaS exposes the Carrefour-DPP API and starts metering.
Procurement & Energy
→
R&D Triage
Acrylic adhesive cost up 9% on hedge curve — Triage elevates CleanFlake (less adhesive load) in the pipeline.
Section 6 · Agent map & registry
SCIKIQ Agent Mesh — registry, identity & control plane for the agent fleet
The SCIKIQ Agent Mesh treats every agent as a first-class workforce identity — registered, discoverable, governed and observable. Every agent is mapped to a pillar, owned by a persona, grounded in the ontology, and wired into the same guardrails as a human user.
SCIKIQ
Agent Registry & Control Plane
IdentityCatalogPolicyTelemetry
Pillar 01 · Revenue Growth
NPI Scout
R&D Triage
Voice-of-Customer
Patent Watch
Pillar 02 · Data Monetization
Data Product
Benchmark
Contract & Consent
Insight-as-a-Service
Pillar 03 · EBITDA Margin
Yield Optimizer
OEE / Throughput
Cost-Driver
Procurement & Energy
Connects to
- M365 / Copilot · Teams, Outlook, Excel, SharePoint
- ERP & MES · SAP, Oracle, Rockwell, AVEVA
- CRM · Salesforce, Dynamics
- Data plane · Fabric, Snowflake, atma.io
- Other agents · A2A handoff via shared ontology
Agent registry — like a workforce directory, for agents
| Agent | Pillar | Owner persona | Grounding | Tools / systems | Trigger | Guardrails | Status |
|---|---|---|---|---|---|---|---|
| NPI Scout | Revenue | CGO | Ontology · Customer 360 · DDGS | CRM · DDGS · KB | Weekly + event | Obs · Gov · Sec | Live |
| R&D Triage | Revenue | CTO | R&D pipeline · IP graph | PLM · Patents API | Weekly | Obs · Gov · Sec | Live |
| Voice-of-Customer | Revenue | CMO | CRM notes · Tickets · NPS | Salesforce · Zendesk | Daily | Obs · Gov · Sec | Live |
| Patent Watch | Revenue | CTO | IP ontology · Competitor map | USPTO · EPO feeds | Daily | Obs · Gov · Sec | Pilot |
| Data Product | Data $ | CDO | Data catalog · Contracts | Fabric · atma.io | On request | Obs · Gov · Sec | Live |
| Benchmark | Data $ | CDO | Anonymized aggregate KB | Snowflake · DP layer | Monthly | Obs · Gov · Sec | Live |
| Contract & Consent | Data $ | CLO | Consent ledger · Purpose-of-use | OneTrust · Vault | Every call | Obs · Gov · Sec | Live |
| Insight-as-a-Service | Data $ | CDO | Data products + ontology | API gateway · Stripe | On request | Obs · Gov · Sec | Pilot |
| Yield Optimizer | EBITDA | COO | MES · process tags · recipe lib | AVEVA · Historian | Per shift | Obs · Gov · Sec | Live |
| OEE / Throughput | EBITDA | COO | Downtime codes · scheduling | Rockwell · SAP PP | Per shift | Obs · Gov · Sec | Live |
| Cost-Driver | EBITDA | CFO | COGS bridge · SKU × plant | SAP CO · BW | Weekly | Obs · Gov · Sec | Live |
| Procurement & Energy | EBITDA | CPO | Commodity index · contracts | Coupa · Energy APIs | Daily | Obs · Gov · Sec | Pilot |
Every row in the registry is an identity in SCIKIQ — provisioned, scoped, observable, revocable. Just like an employee record.
Section 7 · End-to-end protection
Guardrails for every agent and every user — Observability, Governance, Security
Agents are workforce. Treat them like one — with identity, policy, audit, and a kill switch. SCIKIQ wraps every agent in the registry with the three rings below, so the same controls apply whether the requestor is a human or an agent acting on a human's behalf.
Observability
See what every agent does, in real time
- Per-agent telemetry — calls, latency, tokens, cost
- Decision trace — prompt → grounding → tool calls → output
- Drift & quality — eval scores against gold answers
- Health dashboard — green / amber / red by agent & pillar
- Issue resolution — replay any session, diff against baseline
Governance
Who's allowed to do what, with which data
- Agent registry — owner, pillar, purpose-of-use on file
- Policy engine — RBAC, data classification, residency
- Approval workflows — high-impact actions need human sign-off
- Audit log — immutable record of every agent action
- Lifecycle — onboard / promote / retire like a person
Security
Protect the agent, the data, and the action
- Workforce identity — every agent has its own credential
- Least-privilege tools — scoped tokens per system
- Prompt-injection defense — input/output filtering & isolation
- Secrets & PII redaction — at ingress and egress
- Kill switch — revoke any agent in one click
Security
Governance
Observability
Agent
grounded in ontology + KB
End-to-end protection
Every request — from a human or another agent — passes through all three rings. Nothing reaches a tool, a system, or the LLM without identity, policy, and trace attached.
- Inputauth · prompt-injection scrub · PII redact
- Reasoninggrounded prompt · cited evidence only
- Actionscoped token · policy check · approval if needed
- OutputPII filter · audit log · telemetry emit
Section 8 · Demo
See the SCIKIQ Agent Mesh in action — a request flowing through the registry, guardrails, and back
Follow one real ask — "Why is HVC margin slipping in Wine & Spirits?" — as it traverses identity, the agent map, grounding, tools, guardrails, and telemetry.
1
User asks
CFO persona · authenticated · SSO + MFA
"Why is HVC margin slipping in Wine & Spirits — and what should I tell the board?"
→
2
Guardrail check
SCIKIQ policy engine
- Identity verified ✓
- Data scope: WS-Finance ✓
- Prompt-injection scrub ✓
- Audit log opened ✓
→
3
Agent map routing
Registry · pillar selection
- Cost-Driver agent (EBITDA)
- + Yield Optimizer (EBITDA)
- + Voice-of-Customer (Revenue)
- A2A handoff via ontology
→
4
Grounded tool calls
Scoped tokens · per agent
- SAP CO → COGS bridge by SKU
- AVEVA → scrap rate, last 8 wks
- Salesforce → top-5 WS accounts
- Ontology → HVC, PSA, LPM
→
5
Structured answer
CFO-persona narrative
Implication: −180 bps, driven by 60% scrap on Line 7
Action: shift Line 7 recipe v3.2 · Owner: SVP LPM · By: end of Q3
Evidence: SAP, AVEVA, CRM chips → drill back
…meanwhile, observability streams the whole run
Latency
8.4s
P50 across 3 agents
Tokens
42k
in · 6.1k out
Cost
$0.31
per answer
Eval
0.94
vs gold answer
Policy
0 deny
all checks passed
Audit
trace #4821
replayable
Same pattern for any of the 12 agents in the registry — one ontology, three pillars, three guardrails, end-to-end traceable.
Section 9 · Commercials
Pricing model — predictable license, pass-through infra, optional accelerator
Three clean line items. The platform license is the only recurring SCIKIQ fee. Infrastructure is consumed and billed directly by the client's cloud. Use-case delivery is an optional CapEx accelerator at go-live.
Recurring · OpEx
Platform License
SCIKIQ agent registry, control plane & standard support
- Term · 3-year or 5-year
- Includes · standard support, version upgrades, security patches
- Scope drivers · # use cases · # sources · # legal entities
- Billing · annual, in advance
Locks unit economics; supports multi-year CFO planning.
Pass-through · OpEx
Infrastructure
Cloud compute, storage, LLM inference — paid directly by client
- Owned by · client's cloud account (Azure / AWS / GCP)
- Covers · compute, storage, network, model inference, observability
- Why direct · no markup, full FinOps visibility, tenant isolation
- Optimization · SCIKIQ tunes cache, batch & model routing to keep cost down
Variable cost scales with usage — fully transparent on the client's bill.
One-time · CapEx
Setup & Use-Case Delivery
Infra setup + optional initial agent build-out
- Infra setup · landing zone, networking, identity, observability wiring
- Optional · initial use-case delivery (one or more agents from the registry)
- Booked as · CapEx, amortizable over license term
- Outcome · production-ready agents at go-live, not slideware
Sized per scope of go-live use cases — fixed-fee, milestone-based.
What every license includes — the SCIKIQ 4 Cs
C
Connect
Plug into any source — ERP, MES, CRM, data lake, files, APIs, feeds.
C
Curate
Build the ontology, KB and taxonomy that ground every agent in real facts.
C
Control
Observability, governance & security guardrails around every agent and user.
C
Consume
Use the agents — via personas, copilots, APIs and embedded experiences.
Licensing models — pick the shape that fits the org
Per Department
License scoped to a single function — e.g. Finance, Manufacturing, or R&D.
- Lower entry point · fastest TTV
- Sized by use cases & sources in scope
- Upgrades to enterprise without re-platforming
Best for: pilot land & expand within one P&L owner.
Most common
Enterprise-Wide
One license covering the full enterprise — all pillars, all departments.
- Unlimited departments under a single legal entity
- Tiered by # use cases & # sources configured
- Cross-pillar agent reuse (Revenue × Data × EBITDA)
Best for: CXO-sponsored, multi-pillar agentic transformation.
Per Legal Entity (LE)
One license per LE — for groups with many subsidiaries, JVs, or regions.
- Clean ring-fenced billing per LE
- Separate data residency & consent boundaries
- Volume tier across LEs at the group level
Best for: holding companies, multi-region groups, regulated entities.
What sizes the license
Use cases
#
agents activated from the registry — by pillar
×
Sources configured
#
systems wired in (ERP, MES, CRM, data plane, feeds)
×
Legal entities
#
if licensed per LE — otherwise enterprise-wide
=
License tier
$
3-yr or 5-yr fixed
Total cost stack at a glance
| Line item | Type | Who pays | Cadence | Includes |
|---|---|---|---|---|
| SCIKIQ Platform License | OpEx | Client → SCIKIQ | 3-yr / 5-yr · annual | Registry, control plane, standard support, upgrades |
| Cloud Infrastructure | Pass-through | Client → Cloud provider | Monthly · usage-based | Compute, storage, LLM inference, observability |
| One-time Setup | CapEx | Client → SCIKIQ | One-time · milestone | Infra setup; optional initial use-case delivery |
No hidden infra markup. No per-seat creep. License scales by what creates value — use cases, sources, entities.
TL;DR
A report tells you what happened.
An agentic system tells you what to do about it — grounded in the same facts you would cite yourself.
The ontology, KB and taxonomy are what make that grounding possible. Without them, the AI is a guessing chatbot. With them, it's a colleague.
An agentic system tells you what to do about it — grounded in the same facts you would cite yourself.
The ontology, KB and taxonomy are what make that grounding possible. Without them, the AI is a guessing chatbot. With them, it's a colleague.