AI with receipts

AI you can defend in front of a client

Clients now ask firms to evidence their AI. DigitalStack360 grounds every answer in your engagement data, shows its evidence and confidence, and declines when it does not know.

The trust problem

Your client is going to ask how the AI got that answer

Buyers have moved past asking whether a firm uses AI. They now ask firms to evidence it: what the model was given, what it produced, and who checked it. Published 2026 research points the same way, most people act on AI output without checking it, and more than half report running into mistakes from unchecked AI. For a firm that sells judgment, one confidently wrong AI answer in front of a client is a reputational risk, not a productivity story.

Most
of people use AI output without verifying it first
Over half
report encountering mistakes from unchecked AI
Rising
client scrutiny of how firms evidence AI capability

Figures reflect published 2026 industry research. Sources available on request.

How our AI behaves

Four principles, enforced by the platform

These are not guidelines the model is asked to follow. They are the conditions an answer has to meet before it reaches you.

01

Grounded

Every answer traces back to named sources in your own engagement data. No answer is assembled from general knowledge about how projects usually go.

02

Evidence attached

Each answer carries its reasoning, its confidence, and the evidence behind it. You can open the receipt and see exactly what the conclusion rests on.

03

Human review before apply

AI output lands as a reviewable draft. Someone reads it, edits it, and approves it. Nothing changes in your engagement silently.

04

Declines rather than guesses

When the platform cannot ground an answer in real evidence, it says so instead of producing something plausible. A clear decline is a feature.

What an answer looks like

Not a paragraph. A claim with its receipts.

A grounded answer states what it believes, how confident it is, and which specific items in your engagement produced that view. Anyone can check the reasoning in seconds, including the client sitting across the table.

The claim, stated plainly and specifically
A confidence level you can weigh
The named evidence behind the conclusion
The authority inside the platform that contributed it
Delivery concern
Confidence: High

Milestone 3 is likely to slip. Two blocking dependencies have been open more than 8 days.

Evidence: JIRA-482 blocked since Jul 14
Evidence: JIRA-505 awaiting client input
Contributing authority: Delivery Concern engine
Across the lifecycle

Where AI shows up in the work

The same rules apply everywhere. Grounded, evidenced, reviewed, and willing to decline.

Opportunity

Research and scoring

Background research on the account and a scored read on fit, drawn from what you have captured about the opportunity.

Discovery

Survey planning

Proposed discovery questions and survey structure based on the engagement type and what is still unknown.

Discovery

Workshop transcript extraction

Findings, requirements, and decisions pulled out of workshop transcripts, each one linked back to where it was said.

Solution

Solution design assistance

Drafted architecture and approach that reflects the discovery findings and decisions already on record.

Estimation

Estimate analysis and scope quality

Checks on scope completeness and estimate coherence, flagging the gaps and assumptions worth resolving before you commit.

Decisions

Prioritization and report drafting

Open decisions ranked by what is actually blocking delivery, plus drafted reports you review before anything goes out.

Delivery

Risk briefings and forecasts

Delivery concern briefings and forward-looking reads on milestones, each grounded in the signals that produced them.

Documents

Drafting and enhancement

First drafts and improvements for engagement documents, written from your captured context rather than a blank page.

Time

Time entry drafting

Proposed time entries built from the work already visible in your engagement, ready for the person to confirm or correct.

Governed and metered

You control the spend, and you can bring your own key

AI that a firm cannot see or govern becomes a line item nobody owns. DigitalStack360 treats AI usage as something to be measured, bounded, and attributed like any other operating cost.

Metered with admin visibility
AI usage is metered and reported, so administrators can see what is being spent, where, and by which part of the platform.
Rate controls
Rate and usage controls keep AI spend inside the boundaries your firm sets, rather than growing quietly in the background.
Bring your own provider key
Firms can run usage through their own AI provider account, keeping the commercial relationship and the usage record on their side.

See an answer with its receipts

Bring a live engagement question and we will show you the answer, the evidence behind it, and what happens when the platform decides it does not know.