Glassbox AI · Dynamic Observability

Your AI, on the record.

Most AI gives you an answer and asks you to trust it. Cognis opens the box. Every activity in a turn, every byte in the LLM's memory, every model and token spent. You can inspect it, edit it, or release it. Live.

Activity Trail · Memory View · Context Ledger, built into every chat.

The Real Problem

You automated the work.
You didn't automate trust.

AI adoption is everywhere. Trust isn't. Every team running production AI hits the same four walls.

Answers without a trail

The AI gives you a confident reply. You can't see which tool it called, which data it used, or whether it hallucinated the cite.

Context drifts silently

Old turns get compacted. Integration payloads linger. You never see what's actually in the LLM's head on the current turn.

No way to debug a bad reply

When the output is wrong, your only option is re-prompt and pray. No step inspector. No payload view. No memory ledger.

Nothing to hand to compliance

Legal and clients want to know how the AI reached an answer. You can't show them a timeline, a model list, or a source.

Teams now spend more time verifying AI outputs than they saved generating them.

Challenge the Status Quo

Smarter models don't fix
a trust problem.

A faster model doesn't show you its activity. A bigger context window doesn't tell you what's taking up space. "Just re-prompt" isn't a debugging strategy.

"Which tool actually produced this number?"

No activity trail visible in chat.

"What is the LLM remembering right now?"

Memory is opaque, buried, or not a thing.

"Where in the turn did it go wrong?"

Nothing to inspect between prompt and reply.

"Can I show this to the auditor?"

No exportable ledger of models, tokens, sources.

Every team finds errors after the fact, instead of preventing them upfront.

The Reframe

AI doesn't fail because models aren't smart enough. It fails
because
you can't see what they're doing.

01
Per-message

Activity Trail

Every assistant message expands into the activities that produced it: reasoning steps, tool calls, agent handoffs, human-in-loop pauses. Tap any row to see the payload, the model, the tokens spent, and the integration used.

02
Live · editable

Memory View

A live window into what the LLM is holding right now: active messages, running summary of compacted turns, loaded skills, plus every integration payload (Google Sheet rows, Semrush reports, CRM pulls). Release anything that's stale. Watch token usage drop on the next turn.

03
Per-turn

Context Ledger

Every run records which models were called, what they cost, how many tokens were spent (cached vs fresh), and whether the turn completed or was interrupted. Exportable for compliance, finance, or debugging.

04
Your call

Context Controls

Smart summarization with manual override. Pin facts that must never be compacted. Forget anything that shouldn't travel. Nothing about your AI stays invisible.

The cost angle

Visibility pays for itself.

See what the LLM is holding. Release what it isn't using. Watch the token count drop, and your bill with it.

M
Memory View · Acme deal thread
Live, updates as the conversation runs
4,616 / 8,000 tokens
Loaded skill· auto-loaded on intent match
Sales analyst agent
System prompt + 7 example plays for revenue diagnosis.
420 tok
Running summary· oldest turns compressed to free context
8 earlier turns compacted
"Investigating Q3 miss for Acme. Identified renewal risk, EMEA churn, pricing test. Draft under review."
310 tok
Active message· turn #12 · kept verbatim
Your last question
"Why did Acme's Q3 revenue drop?"
42 tok
Google Sheets payload· pulled at turn #10 · still in context
Q3 Pipeline.xlsx · 42 rows
Opportunity, Amount, Stage, CloseDate. 42 rows ≈ 2.1K tokens. Still loaded on every LLM call.
2180 tok
Semrush payload· pulled at turn #7 · not referenced since
acme.com: organic traffic 90d
domain_ranks + top 50 keywords. Last referenced 5 turns ago.
1640 tok
Pinned fact· you pinned this · Oct 14
Procurement requires SOC 2 Type II
Travels with every message. Never compacted. Never dropped.
24 tok
Release anything the LLM doesn't need anymore. Fewer tokens, sharper answers, lower cost per turn.

Smaller context, smaller bill.

Every token the LLM carries is a token you pay for. The Memory View above shows exactly what's loaded: Semrush reports from 8 turns ago, a 14-row Google Sheet that's already been summarized, a PDF you only needed once. Release them and the bill drops. Immediately.

~30%
of context released by turn 10 in typical research chats.
1:1
tokens released = tokens not billed. No proxy math.
Compounds across the conversation

A payload released on turn 5 isn't re-sent on turns 6, 7, 8… Long chats are where the savings stack up, and long chats are where other tools quietly lose track.

Side-by-Side

The only multi-LLM platform that makes AI verifiable at the chat layer.

Comparing platforms built on top of the model vendors. The category Cognis actually competes in.

Capability
Cognis Ai
Dust.tt
Lindy.ai
T3 Chat
Poe
Activity Trail visible in chat
dev logs only
dev logs only
Live Memory View with release controls
learns silently
Integration payloads inspectable in-turn
Per-turn Context Ledger (tokens, models, cost)
enterprise
Pin / release controls for facts
agent-level

Other tools have observability for developers. We built it for the person actually using the AI. See how Multi-LLM Intelligence works →

Why Cognis

AI you can verify before you act.

No black-box agents. No "just trust it." No hidden hallucinations.

Glassbox AI is built so the person using the model knows exactly what it's doing on every single turn.

Observability at the chat layer

Not buried in developer logs. Visible to the person actually using the AI, in the moment.

User-controlled context

You decide what the LLM sees on every turn. Release, pin, summarize, override.

Editable live memory

Correct the AI by pruning its memory, not by re-prompting from scratch.

Full audit trail

Every activity, every model, every token, every source. Exportable for compliance.

This is for you if you…

If "the AI said so" is good enough for your work, Cognis probably isn't for you.

◎ Related features

Keep exploring the chat layer.

This feature works because of one, and enables the other. Both pair with it in real workflows.

AI is everywhere.
Trust shouldn't be optional.

See the Activity Trail. Prune the Memory View. Read the Context Ledger. Ship with confidence.

Generous free tier · No credit card · Observability on by default