The Context Graph: Why Recording Outcomes Is Not Enough - and How a Company Brain Captures the Reasoning Behind Every Decision

11 July 202633 min read

Co-founder at Superkind

A connected network of decision nodes representing a company context graph

Ask your ERP what a product sold for last quarter and it answers instantly. Ask it why the sales lead gave that customer an extra 4 percent off, and it has nothing. The number is there. The reasoning is gone - it lived in a Slack thread, a hallway conversation, and the head of a rep who left in March.

Enterprise software spent thirty years getting very good at recording outcomes: the final price, the approved discount, the escalated ticket, the shipped order. It is terrible at recording the reasoning behind them. That reasoning is the most valuable asset your company produces every day, and almost none of it is being captured. Foundation Capital calls the missing layer a context graph, and argues it is the next trillion-dollar category in enterprise software¹.

This guide is for the operations leader, CTO, or managing director who has watched an expert retire and take twenty years of judgement with them, or seen an AI pilot stall because the agent knew the rules but not the exceptions. No hype. Here is what a context graph is, why your current systems will never build one, and how a Company Brain captures the reasoning behind how your company actually decides - so it survives turnover instead of walking out the door.

TL;DR

Systems of record store outcomes, not reasoning - your ERP and CRM know what happened, never why the decision was made.

The reasoning lives outside your systems - in Slack, email, side conversations, and people’s heads, where nothing indexes it and it leaves when they do.

A context graph is institutional memory for how your company decides - a living, queryable record of decisions and the reasoning behind them, not how the process doc says it should work.

It is not a wiki, a knowledge graph, or plain RAG - it is fed by daily work, captures exceptions and approvals, and stays current instead of decaying.

A Company Brain builds and maintains it - observing real work and feedback - and AI employees then act on top of it across email, Teams, SharePoint, CRM, and ERP.

The Outcome Trap: Your Systems Record What, Never Why

Every system you run is a system of record for outcomes. It captures the state of the business after a decision has been made, and it captures it well. What it never captures is the decision itself - the weighing of options, the exception that was justified, the precedent that governed the call. That reasoning is treated as exhaust, not data.

The ERP records the transaction - it stores that purchase order 4471 was approved at 84,000 euros. It does not store why the buyer chose the pricier supplier: a quality history and a lead-time guarantee that only the buyer knew about.
The CRM records the close - it shows the deal won at a 12 percent discount. It does not show that the discount was a strategic loss-leader to displace a competitor before a larger renewal, a rationale that lived entirely in the rep’s head.
The ticketing system records the resolution - it marks the case closed. It does not record the undocumented workaround the senior agent used, which the next agent will have to rediscover from scratch.
The accounting system records the posting - it shows the invoice coded to a cost centre. It does not show the judgement call about which project the ambiguous line item belonged to.
The MES records the parameter change - it logs that the setter adjusted the temperature. It does not log why, or which material batch made the change necessary.

As Foundation Capital puts it in their thesis, the problem is structural, not a matter of discipline: enterprise software is very good at recording outcomes but not the reasoning behind them, because that reasoning was never treated as data in the first place¹.

The Core Distinction

A system of record stores objects and outcomes: the order, the price, the ticket, the posting. A context graph stores the reasoning that connects inputs to outputs: the exception, the approval, the precedent, the why. The last generation of enterprise software became trillion-dollar platforms by becoming systems of record. The next generation becomes systems of record for decisions¹.

Business Event	What Your System Records	The Reasoning It Loses
Supplier chosen	PO approved at a set value	Why this supplier over a cheaper one
Discount granted	Deal won at 12% off	The strategic reason for the concession
Ticket resolved	Case marked closed	The workaround that actually fixed it
Exception approved	Status set to approved	Who decided, and on what precedent
Invoice coded	Line posted to a cost centre	The judgement behind the ambiguous case

This is the outcome trap. You have a perfect record of what your company did and almost no record of how it decided to do it. And the how is exactly what a new hire needs, what an auditor asks for, and what an AI agent must have to be trusted with real work.

What a Context Graph Actually Is

A context graph is institutional memory for how an organisation makes decisions: not how the process document says it should work, but how it actually works in practice¹. The building block is the decision trace - a record that captures not just the decision that was made, but the context, reasoning, exceptions, and approvals that led to it⁶.

Individual decision traces accumulate into a graph: a living, queryable map of how the enterprise decides, stitched across systems and over time, so precedent becomes searchable¹. Where a data warehouse answers “what is true,” a context graph answers “how do we handle this here, and how have we handled it before.”

The anatomy of a decision trace

The trigger - what prompted a decision: an order arrived, a threshold was breached, a customer complained.
The context - the state of the world at that moment: the customer’s history, the current inventory, the relevant policy, the competing constraints.
The reasoning - the weighing of options and the rationale for the choice, including what was rejected and why.
The exception - where the standard rule did not apply, and the justification for departing from it.
The approval - who signed off, at what level of authority, and against which precedent.
The outcome - the action that resulted, linked back to the system of record that stored it.

Capture those six elements as work happens and connect them across time, and you get something enterprises rarely have: a structured, replayable history of how context turns into action⁶. Decision traces provide searchable records of how situations have been handled before, letting people - and agents - ground their reasoning in institutional reality rather than guessing⁵.

Why This Matters Now

Gartner predicts that by 2028, context engineering features will be built into 80 percent of software tools used to build AI applications, and that context engineering will improve agentic AI accuracy by at least 30 percent through 2028⁵. Gartner also frames context as the new critical infrastructure for AI - agents cannot operate reliably without understanding business context beyond raw data¹⁹.

“The reasoning connecting data to action was never treated as data in the first place.”

- Jaya Gupta, Partner at Foundation Capital¹

That single sentence explains why the most valuable knowledge in your company is also the least captured. It was never anyone’s job to write it down, and no system was built to hold it.

Where the Reasoning Actually Lives

If your systems of record do not hold the reasoning, it has to live somewhere. It lives in the least reliable storage medium your company owns: unindexed conversations and individual memory. Every one of these is a place your context graph should be capturing from, and today captures nothing.

Slack and Teams threads - “quick question, can we make an exception for this account?” followed by a yes and a reason. The decision is made, the thread scrolls away, and the rationale is gone within a week.
Email chains - the back-and-forth where a contract term was negotiated and a concession justified. Buried in an inbox no successor can search.
Hallway and call conversations - the verbal “just do it this way for this customer, they are a special case” that never touches a keyboard.
People’s heads - the senior employee who simply knows which supplier to trust, which customer to never put on credit hold, and which machine needs a lighter touch. Research finds 42 percent of institutional knowledge is unique to the individual who holds it¹².
Meeting decisions that never get minuted - the call gets made, everyone nods, and no artefact records why.
Spreadsheets with tribal logic - the pricing model whose adjustment factors only one person can explain.

This is why documented process and real process diverge. Process mining exists precisely because the way a process is defined and the way it actually runs are very different stories - conformance checking reveals where steps are skipped, prolonged, or executed out of order compared to the official model¹⁸. Your process document describes the intended path. The context graph should describe the real one.

Today: Fragile Storage

✗ Chat threads - scroll away and are never searched again
✗ Individual memory - leaves the building at 5pm and forever on the last day
✗ Email inboxes - private, unindexed, lost on offboarding
✗ Verbal decisions - no artefact at all

Should Be: A Context Graph

✓ Captured in flow - recorded as the decision is made, not reconstructed later
✓ Queryable - precedent is searchable across the whole company
✓ Durable - survives when the person who decided moves on
✓ Linked - reasoning connected back to the outcome in the system of record

What reasoning is your company losing every day?

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The Cost of Lost Context

Losing the reasoning behind decisions is not a soft, unmeasurable problem. It shows up as wasted hours, botched onboarding, repeated mistakes, and, when key people leave, a permanent hole in how the company operates. The numbers are large and consistent.

$47 million a year - the average large US business loses this much in productivity from inefficient knowledge sharing, split between roughly $42.5 million in lost productivity and $4.5 million in inefficient onboarding¹².
$31.5 billion a year - the amount Fortune 500 companies collectively forfeit from failing to share knowledge, according to IDC estimates¹².
5.3 hours a week - the time each knowledge worker wastes waiting for information from colleagues or recreating knowledge that already exists¹³.
Up to 50 percent of company knowledge is unsearchable - forcing employees to duplicate effort rather than build on what was already figured out¹².
47 percent of knowledge workers struggle to find the information they need to do their jobs¹⁶.
Two-thirds of IT leaders are concerned about organisational knowledge loss from employee turnover specifically¹⁷.

The Turnover Time Bomb

Organisations expect around half of their workforce to retire or move on within five years, yet 41 percent of organisations rarely or never even attempt to collect know-how from people who are leaving¹⁵. When an expert walks out with the reasoning behind a decade of decisions, a new hire has to start essentially from scratch - which is exactly what 41 percent of employees report happening to them¹⁴.

The knowledge you lose is not the outcomes - those are safely in the ERP. It is the reasoning: the judgement, the exceptions, the precedent. That is the layer a context graph is built to keep.

Symptom of Lost Context	Cost	Source
Knowledge-sharing inefficiency	$47M/year per large business	Panopto¹²
Failure to share knowledge	$31.5B/year across Fortune 500	IDC via Panopto¹²
Time lost per worker	5.3 hours/week	Panopto¹³
Knowledge unique to individuals	42% of institutional knowledge	Panopto¹²
Workers who cannot find info	47% of knowledge workers	Stravito¹⁶

Concentric layers representing accumulating institutional memory in a company brain

Context Graph vs Wiki, Knowledge Graph, and RAG

“We already have that” is the most common reaction, usually pointing at a wiki, a knowledge graph project, or a shiny new RAG chatbot. Each captures something real. None of them captures the reasoning behind decisions the way a context graph does. Being honest about the difference is the point.

Against the static wiki and SharePoint

Written once, decays from day one - a wiki page reflects the moment it was written and is never updated as the work changes. A context graph is fed continuously by the work itself.
Describes intent, not reality - documentation captures how a process is supposed to run. It cannot capture the exceptions that make up real operations.
Nobody maintains it - the classic wiki failure. A context graph maintains itself by observing decisions, not by asking someone to remember to write them down.

Against the knowledge graph

Entities vs reasoning - a knowledge graph maps things and their relationships: customer to order, order to product. It is powerful for multi-hop and audit questions⁸, but it stores the state of the business, not how the business decides.
A context graph adds the decision layer - it records why this customer got that term, which precedent applied, and who approved it. The two are complementary: a mature context layer is often built on a graph foundation⁴.

Against plain RAG

Similarity is not reasoning - vector search finds text that looks like your query. It cannot capture relationship structure or how a decision was reached².
The multi-hop ceiling - RAG and memory without a graph can retrieve and persist, but cannot reason across explicit decision relationships⁷.
The best architectures combine all three - by 2026 the consensus is hybrid: vector search for entry points, graph traversal for connected context, and persistent memory for session and decision history⁷. An estimated 85 percent of enterprises are moving to hybrid retrieval combining vector and graph²⁰.

Capability	Static Wiki	Knowledge Graph	Plain RAG	Context Graph
Captures reasoning behind decisions	No	Partial	No	Yes
Records exceptions and approvals	Rarely	No	No	Yes
Stays current automatically	No	Needs modelling	Re-index only	Yes (fed by work)
Reasons across relationships	No	Yes	Limited	Yes
Survives staff turnover	If updated	Partial	Partial	Yes
Grounds an AI agent at decision time	Weak	Partial	Partial	Yes

“A system of record for decisions, not just data.”

- Dharmesh Shah, Co-founder and CTO of HubSpot¹

How a Company Brain Builds and Maintains the Context Graph

A context graph is not something you buy pre-filled. It is your company’s memory, so it can only be built from your company’s work. The mechanism that builds and maintains it is what we call a Company Brain: a living memory layer fed by daily work and feedback, sitting on top of the systems you already run. Here is how it fills the graph without turning into another documentation project nobody finishes.

Observe decisions in the tools where they happen - the Company Brain connects to email, Teams, SharePoint, CRM, and ERP, and watches the decisions flow: the discount granted, the exception approved, the ticket resolved. Capture happens in the flow of work, not in a separate wiki.
Capture the reasoning, not just the outcome - when a decision has a rationale attached (a Slack reply, an email justification, a note), the Brain links that reasoning to the outcome and stores it as a decision trace.
Confirm the ambiguous cases with a light human check - where the reasoning is unclear, the Brain asks a one-line question: “Was this a one-off, or the new rule for this customer?” A single tap turns a guess into durable precedent.
Learn from every correction - when someone overrides how a decision was recorded, that correction feeds back in. This is the feedback loop that makes the graph sharper every week instead of staler.
Link precedent across time and systems - the Brain connects today’s decision to the similar ones before it, so “how do we handle this” returns real history, not a single stale note.
Keep it under your control - the graph is your most sensitive asset. It can run within your own infrastructure or on EU soil, with access controls and audit logs from day one.

Why It Does Not Decay

A wiki decays because maintaining it is a separate task competing with real work, and real work always wins. A Company Brain does not have that problem: maintenance is the by-product of the work. Every decision made in your systems is a deposit into the graph. The asset compounds precisely because nobody has to remember to feed it.

What a Healthy Context Graph Captures

The reasoning behind pricing and discount decisions, linked to each deal
Exceptions granted and the precedent that justified them
Who approved what, at which level of authority
The undocumented workarounds that actually resolve recurring issues
Supplier and customer judgement calls that today live only in one head
The rationale behind coding, classification, and routing decisions
How similar cases were handled the last five times they came up
Corrections and preferences captured as people refine the record

How AI Employees Act on Top of the Context Graph

A context graph is not an end in itself. Its value is that it lets both people and AI employees act with the judgement of your best staff. Without it, an AI agent behaves like an extremely capable intern on day one - it can follow the written rules but gets tripped up by every unwritten exception¹. With it, the agent grounds its reasoning in how your company actually decides.

In customer support - an AI employee resolving a refund request queries the graph for how similar edge cases were handled, applies the same reasoning, and routes the genuinely novel one to a human - instead of inventing a policy.
In finance - an AI employee coding an ambiguous invoice checks the precedent for that supplier and cost centre, applies the established judgement, and flags only the truly new case.
In sales operations - an AI employee preparing a quote sees the reasoning behind past discounts for that account tier and stays inside the real, precedent-backed guardrails.
In procurement - an AI employee drafting a purchase recommendation weighs the same supplier-quality history a senior buyer would, because that history is now in the graph rather than in the buyer’s memory.
Across the stack - the same AI employee executes the last mile in the real systems: it writes back to the CRM, posts in the ERP, updates the SharePoint record, and replies in email or Teams.

This is the difference between a demo that impresses and an AI employee you can trust in production. Decision traces let agents ground their reasoning in institutional reality rather than statistical inference alone⁵, and the same instrumentation that records those traces also powers the feedback loop that keeps improving them.

The Compounding Advantage

Two competitors buy the same off-the-shelf AI tool. One connects it to a context graph built from years of their own decisions; the other runs it on generic rules. Within months the first company’s agents handle exceptions correctly and the second’s keep escalating. The graph is the moat - it is built from your work and cannot be copied, downloaded, or bought by anyone else.

With a Context Graph

✓ Handles exceptions - grounded in real precedent
✓ Consistent with your judgement - decides the way your best people do
✓ Improves weekly - every correction sharpens the graph
✓ Auditable - every action links to the reasoning behind it

Without One

✗ Fails on edge cases - guesses from statistical patterns
✗ Inconsistent - invents policy the company never agreed
✗ Static - makes the same mistake every time
✗ Opaque - cannot explain why it acted

Building a Context Graph in 90 Days

You do not build a company-wide context graph in one go, and you should not try. Pick one function where lost reasoning hurts most, capture it well, and let the graph prove its value before you expand. Here is a focused 90-day path.

Phase 1: Pick the bleeding function (Weeks 1-3)

Week 1: Find where reasoning leaks - identify the function where decisions carry the most judgement and the most turnover risk: often support, pricing, or finance operations.
Week 2: Map the decision types - list the recurring decisions in that function and, for each, what reasoning is currently invisible to your systems.
Week 3: Connect the sources - wire the Company Brain to the systems where those decisions happen and their reasoning lives: CRM, ERP, ticketing, email, Teams.

Phase 2: Capture and confirm (Weeks 4-8)

Weeks 4-5: Start observing - the Brain begins recording decision traces as work happens, with a lightweight confirmation step for ambiguous cases.
Weeks 6-7: Tune the capture - refine what counts as a decision worth recording and which reasoning matters, so the graph fills with signal, not noise.
Week 8: Validate against real cases - test the graph by asking it how recent tricky cases were handled, and check the answers against the people who handled them.

Phase 3: Act and expand (Weeks 9-12)

Week 9: Put an AI employee on top - let an agent use the graph to handle routine decisions, escalating the novel ones, running in parallel with your team.
Weeks 10-11: Measure - track time saved, consistency of decisions, and how many cases the graph now answers without a human.
Week 12: Expand to the next function - carry the same pattern into the adjacent workflow, reusing the connectors and the feedback loop.

Context Graph Readiness Checklist

You can name a function where key decisions rely on judgement, not just rules
You have felt the pain of an expert leaving and taking knowledge with them
Your reasoning today lives in Slack, email, and heads, not in your systems
Your core systems have API access or connectors
You have a process owner who will confirm and correct captured decisions
Leadership treats decision knowledge as an asset worth keeping
You are willing to start with one function, not the whole company
You have a plan for where the graph runs and who can access it

How Superkind Fits

Superkind builds custom AI employees for SMEs and enterprises, and every one of them runs on a Company Brain - the living memory layer that becomes your context graph over time. The approach is process-first: we start with how your team actually decides, not with a generic product you have to adapt to.

Process-first discovery - we come into your company and talk to the people who do the work, mapping the real decisions and the reasoning behind them before building anything.
A Company Brain, not a static index - the memory layer captures the reasoning behind decisions as work happens, so it becomes a context graph that reflects how your company decides today.
Sits on top of your stack - one layer over everything you already use: email, Teams, SharePoint, CRM, ERP, and custom APIs. No rip-and-replace, nothing new to learn.
AI employees that act on the graph - agents ground their decisions in your captured precedent and execute the last mile in your real systems, not in a chat window.
A feedback loop by design - every correction your team makes feeds back in, so the Brain and the agents get better every week.
Survives turnover - because the reasoning is captured as people work, it stays in the company when they leave, and a new hire or a new agent can query it on day one.
Outcomes, not licenses - pricing is per use case with measurable ROI defined before the build, not seat licenses and multi-year lock-ins.
Sovereign by default - your context graph is your most sensitive asset, so it can run on EU soil under your control, with encryption, access controls, and audit trails.

Approach	Generic AI Tool	Superkind Company Brain
What it grounds on	Documents and generic rules	Your captured decision reasoning
Handles exceptions	Guesses or escalates	Applies real precedent
Stays current	Manual re-indexing	Fed by daily work and feedback
Survives turnover	No - knowledge leaves too	Yes - reasoning stays in the graph
Where it runs	Vendor cloud, often US	Your infrastructure or EU soil
Pricing	Seat licenses	Per use case, tied to outcomes

Pros

✓ Builds a real context graph - captured from your work, not bought off a shelf
✓ No platform lock-in - works on top of your existing systems
✓ Knowledge that survives turnover - reasoning stays in the company
✓ Outcome-based pricing - pay for results, not seats
✓ Sovereign option - run it on EU soil under your control

Cons

✗ Not a self-serve product - requires engagement with our team
✗ Needs decision access - we have to see how you actually decide, not just docs
✗ Value compounds over time - the graph is thin in week one and rich by month three
✗ Overkill for pure rule-based work - if there are no judgement calls, you may not need it

Decision Framework: Do You Need a Context Graph?

Not every company needs to build one right now. Here is a straight framework to decide whether captured decision reasoning would pay off for you.

Signal	What It Means	Action
Key decisions depend on a few experienced people	High exposure to knowledge loss	Start capturing that reasoning now, before they leave
An AI pilot stalled because it could not handle exceptions	The agent lacked institutional memory	Give it a context graph before trying again
New hires take months to reach real productivity	Precedent is trapped in heads, not queryable	Capture reasoning so ramp time drops
The same decisions get re-litigated repeatedly	No searchable precedent exists	Build the decision memory for that workflow
Auditors ask how a decision was reached	You cannot reconstruct the reasoning	A context graph provides the record-keeping
Your work is purely rule-based with no judgement	Little reasoning to capture	Simple automation is enough - skip the graph for now

Capturing Now

✓ Compounding asset - the graph is richer every quarter it runs
✓ Turnover insurance - reasoning is captured while the expert is still here
✓ AI-ready - agents have real precedent to ground on from day one
✓ Audit-ready - decisions come with their reasoning attached

Waiting

✗ Reasoning keeps leaking - every day of decisions goes uncaptured
✗ Knowledge walks out - each departure is a permanent loss
✗ AI stays stuck - agents keep failing on exceptions
✗ Competitors compound - a rival capturing now pulls ahead

Frequently Asked Questions

A context graph is institutional memory for how your company actually makes decisions - not how the process document says it should. Where an ERP or CRM records the outcome (the final price, the approved discount, the escalated ticket), a context graph records the reasoning behind it: the exception that was granted, who approved it, which precedent applied, and why. It is a living, queryable map of decisions stitched across your systems and over time, so precedent becomes searchable instead of trapped in someone's head.

A knowledge graph maps entities and their relationships - customers, orders, products, and how they connect. A context graph adds the missing layer: the reasoning that connects inputs to outputs. It does not just record that an order was discounted; it records why the discount was granted, what exception justified it, and who signed off. A knowledge graph tells you the state of the business. A context graph tells you how the business got there and how it decides.

No. Retrieval-augmented generation (RAG) finds text that looks similar to a query and hands it to a model. It retrieves documents, but it does not understand relationships or capture how decisions were actually made. A context graph is relational and decision-aware: it records the exceptions, approvals, and precedents that govern reality. The strongest 2026 architectures combine vector search, a graph layer, and persistent memory - RAG alone hits a ceiling because it can retrieve but cannot reason across explicit decision paths.

They were designed as systems of record for transactions, not for decisions. An ERP stores that a purchase order was approved at a certain value; it does not store why the buyer chose that supplier over a cheaper one, or which quality history justified the premium. The reasoning connecting data to action was never treated as data in the first place, so it lands in Slack threads, hallway conversations, and email chains that no system indexes.

It observes the work. As your team resolves tickets, approves invoices, prices deals, and handles exceptions inside the tools they already use, the Company Brain captures the decision and the reasoning around it, then confirms ambiguous cases with a quick human check. Every correction and preference feeds back in. Over weeks the graph fills with real precedent, and because it is fed by daily work rather than a documentation project, it stays current and survives turnover.

That is the entire point. When knowledge lives only in a person's head, it walks out the door on their last day. A context graph captures the reasoning behind their decisions as they work, so the precedent stays in the company. A new hire - or an AI employee - can query how similar cases were handled before, instead of starting from scratch. This is how a company breaks the link between staff turnover and knowledge loss.

No. A context graph sits as a layer on top of your existing email, Teams, SharePoint, CRM, and ERP through APIs and connectors. It reads the decisions happening in those systems and records the reasoning around them. Nothing gets ripped out and your team learns no new platform. The graph is an operational memory layer that complements your systems of record, not a replacement for them.

Documentation is written once and decays from the day it is saved, because nobody updates it and it never observes the actual work. Studies find institutional knowledge is often unique to the individual and up to half of company knowledge is effectively unsearchable. A context graph is fed continuously by the work itself, so it reflects how decisions are made today, not how someone described them at a moment in the past. It is a living memory, not a static wiki.

It can be, if built correctly. The graph can run within your own infrastructure or on EU soil, with encrypted connections, role-based access, and audit logs. Because it records the reasoning and approvals behind decisions, it actually helps with EU AI Act Article 12 record-keeping and with demonstrating how automated decisions were reached. As with any system touching personal data, you scope it with a DPIA and apply data minimisation.

A focused build targeting one function - support, finance, or sales operations - typically shows useful precedent within 6 to 8 weeks and measurable results within 90 days. The graph compounds: the more decisions it observes, the better it answers "how do we handle this here." Unlike a documentation project that is stale before it is finished, a context graph gets more valuable every week it runs.

They can run, but they behave like a smart intern on day one - able to follow written rules but tripped up by every unwritten exception. Without institutional memory, an agent guesses at edge cases from statistical patterns rather than grounding its reasoning in how your company actually decides. A context graph gives the agent searchable precedent at the point of decision, which is the difference between a demo that impresses and an AI employee you can trust in production.

You do. The graph is your company's memory and should stay under your control, ideally within your own infrastructure or an EU-jurisdiction environment. Maintenance is not a manual chore - it is the by-product of daily work plus a lightweight feedback loop where people confirm or correct how a decision was recorded. A process owner typically oversees quality, but the graph maintains itself by observing the work rather than requiring a team to keep it up to date.

A decision trace is the building block of a context graph. It is a structured record of a single decision that captures six things: the trigger that prompted it, the context at the time, the reasoning behind the choice, any exception and its justification, who approved it, and the outcome linked back to your system of record. Individual traces accumulate over time into a queryable graph, so that asking how a similar case was handled returns real precedent rather than a guess.

Sources

Henri Jung

Co-founder of Superkind, where he helps SMEs and enterprises deploy custom AI agents that actually fit how their teams work. Henri is passionate about closing the gap between what AI can do and the value it creates in real companies. He has seen too many companies lose decades of judgement the day an expert retires, and believes the reasoning behind decisions is the most valuable asset a company can keep. He believes the Mittelstand has everything it needs to lead in AI - it just needs the right approach.

Ready to capture the reasoning your company is losing?

Book a 30-minute call with Henri. We will find where your decision knowledge leaks and outline how a Company Brain captures it - no commitment, no sales pitch.