Sage — product intelligence with integrity
Your product organization already has enough opinions. Sage finds the ones worth betting on.
It connects customer evidence, research, strategy, decisions, experiments, roadmaps and delivery — then uses that evidence to challenge assumptions and surface weak bets before they become expensive ones.
Grounded in your evidence · Auditable ·
Marta V.
Draft the opportunity for OBJ-1.
Sage
I can’t draft this one yet. OBJ-1 has no evidence attached, and its stated cause — onboarding is too hard — appears in the objective description and nowhere else in your workspace. Drafting from that would give you a well-formed opportunity resting on a single unsourced sentence.
Three things would change that: 41 support items mentioning setup or access, the 14 churn interviews in your February folder, or any sales note that names the cause.
Step 1 of 7
The state of play
You already have the evidence. It just isn't in the room when the decision gets made.
Interview transcripts, experiment results, support exports, KPIs and strategy decks live in separate systems. So the meeting runs on whoever argues best.
Roadmaps running on faith
Assumptions harden into commitments. Nobody goes back to check whether the evidence that justified them still exists — or ever did.
Knowledge that gets uploaded and forgotten
Research lands somewhere, gets read once, and never connects to the artifact it was supposed to inform. The next decision starts from zero.
AI that bluffs when the evidence runs out
When retrieval fails or support is thin, most assistants still write fluently. You need the opposite: a visible gap, disclosed.
A different starting point
Most AI starts with a prompt. Sage starts with evidence.
Ask a typical AI, “Should we build this?” and it will probably give you an answer.
Sage has a harder job. It has to find the evidence behind the idea, determine whether that evidence is current, separate assumptions from corroborated claims, look for contradictions, understand what other artifacts depend on the decision — and tell you when the evidence simply is not strong enough.
Because a plausible answer isn’t necessarily a good decision.
How Sage works
Retrieval finds documents. Sage builds an evidence system.
1
Ingest
It reads what your organization knows.
Interviews, surveys, support exports, strategy documents, spreadsheets and product artifacts are broken into statements, claims and evidence. Nothing arrives pre-trusted.
2
Connect
It remembers what supports what.
Sage maintains semantic retrieval and a product knowledge graph connecting evidence to personas, opportunities, solutions, experiments, KPIs, epics and roadmaps.
3
Reason
It reasons over the evidence.
Sage can search the corpus, inspect the graph, analyze evidence, compare alternatives, detect patterns and use specialized product-management skills before it constructs an answer.
4
Watch
It keeps looking when you aren't asking.
The background analyst sweeps for contradicted assumptions, untested claims, orphaned evidence, stale commitments, decayed confidence and conflicting solutions.
5
Remember
It keeps what you worked out last time.
Finished questions and answers go back into the corpus. The reasoning your team did in March is retrievable evidence in September. It stops being a scrollback nobody can find.
Ask harder questions
Questions product leaders actually need answered.
“Which roadmap bets are resting on assumptions rather than evidence?”
Inspect the evidence behind each commitment. A roadmap is a set of claims, not a list.
“What do we believe that our own research contradicts?”
Surface contradictions that are easy to miss when knowledge is spread across documents and teams.
“What important decisions are relying on stale evidence?”
Account for freshness and supersession. A two-year-old interview does not vote like this quarter's.
“What are we treating as validated that actually isn't?”
Distinguish what has merely been claimed from what has actually been corroborated.
“Why do we believe this opportunity matters?”
Trace backward from the artifact to the accepted evidence behind it.
“We shipped it. The KPI moved. Did we actually cause it?”
Inspect related changes and confounders before claiming impact.
Productive friction
An AI that is allowed to disagree with you.
Most product software helps work move forward. That creates a dangerous bias: everything eventually becomes a thing to build.
Sage creates friction when the evidence does not justify confidence.
The goal is to make being wrong cheaper.
- Assumptions masquerading as facts
- Contradictory customer evidence
- Opportunities with weak support
- Commitments whose evidence has gone stale
- Solutions that conflict with one another
- Conclusions that outrun what the organization actually knows
Evidence state
It can tell the difference between “we know” and “we think.”
Evidence state travels with the claim all the way into the reasoning. It does not get flattened into context for a language model.
Claimed
Someone has said it.
The assertion exists in the corpus.
Unsupported
The evidence is not there.
The claim exists, but accepted evidence does not support it.
Corroborated
The evidence supports it.
Accepted evidence provides corroboration.
Contradicted
Your own evidence challenges it.
Accepted evidence points in the other direction.
Uncertainty, surfaced
When Sage doesn't know, that matters too.
Confidence should come from evidence. Not from how confidently a model writes.
Sage distinguishes grounded, degraded and exploratory reasoning. If required evidence infrastructure is unavailable, it downgrades the turn and says so.
Grounded
Evidence retrieval succeeded
Degraded
A required capability failed — disclosed
Exploratory
Reasoning extends beyond firm evidence — marked
For whoever owns the roadmap
What actually changes for you.
Product judgment stays yours. Sage removes the option of quietly not exercising it.
1
You can defend the claim in the room
Answers carry their evidence state and are checked assertion by assertion against what retrieval actually returned. You can take the output into a board review without wondering which supporting detail the model invented.
2
Integrity debt shows up early
Contradictions, untested bets and decaying confidence surface on a schedule. By the post-mortem they're free to find and expensive to have missed.
3
Institutional memory stops leaking
Every answered question becomes retrievable context. Turnover costs you people, not the reasoning they did.
4
Confidence becomes explicit
When evidence is thin or a capability is down, Sage says so and marks the turn. You are never left assuming an answer was grounded when it wasn't.
For the technically curious
Each part of Sage has one job.
Different parts of Sage handle routing, retrieval, graph reasoning, document analysis, verification and continuous integrity monitoring.
Reasoning engine
Agentic ReAct tool use over corpus search, graph lookup, evidence analysis, pattern detection, comparison and artifact-generation skills. A self-critic re-runs synthesis when an answer hedges or outruns its support, and a deterministic assertion verifier decomposes the answer into structured statements and checks each one against what retrieval actually returned. Runs stream step by step, so you watch the reasoning rather than a spinner.
Knowledge architecture
Tenant-scoped semantic retrieval in Qdrant plus a Neo4j product graph connecting evidence and product artifacts. Artifact changes dual-write to both stores in one run across 19 entity handlers, each tagged with an evidence tier — validated, indicated or assumed — at write time. Similarity scoring after the write links related artifacts automatically, and causal attribution chains record whether a metric moved because of what you shipped.
Document intelligence
Documents are classified, freshness-checked and atomized into statements and atomic claims carrying polarity, modality, quantifiers and scope. Duplicate, low-integrity, superseded or off-taxonomy material can be halted at any stage, with the reason recorded. Recency decay and supersession detection keep an old deck from carrying a fresh deck's weight.
Continuous integrity analysis
An always-on background analyst sweeps every active workspace for contradicted assumptions, untested claims, orphaned evidence, stale commitments, decayed confidence and conflicting solutions. Each class runs an isolated query, so one failure never breaks the sweep, and findings are ranked high, medium or low. No language model sits in the synthesis path, so the results are deterministic, reproducible and cheap to regenerate.
Model routing and evaluation
A compact fine-tuned router plans the right skill sequence. The stack includes confidence calibration from token logprobs, RLAIF evaluation and a Sage-Bench quality gate against the live reasoning endpoint.
Trust architecture
Your evidence stays yours.
Isolation at the data layer
Per-workspace vector collections and a tenant-aware graph. Scope is enforced server-side and stripped from anything the model supplies.
Injection defenses
Customer-sourced text is handled as untrusted data. A tool result cannot quietly rewrite Sage's instructions.
Evidence state, not model opinion
Claimed, unsupported, corroborated and contradicted are derived strictly from accepted evidence links. Model opinion never sets the state.
Metering you can see
Per-workspace rate limits and usage tracking, so cost and behavior stay observable.
Beyond the copilot