Deliver the spend classification, keep the engagement margin
Category management, sourcing-savings, and spend-cube engagements all start with the same manual bottleneck: classifying a client's messy spend data. Pearstop does that layer, white-labelled under your own taxonomy, so your team spends its hours on the analysis and recommendations clients actually pay for.
Analyst hours are going to data cleanup, not client value
Every new engagement starts the same way: a client hands over years of messy spend data, and someone on your team spends the first weeks just getting it into a usable shape before any real analysis can start. That time comes straight out of engagement margin, and it's the least differentiated part of the work you're being paid for.
- Junior analyst or offshore hours spent on manual classification eat directly into engagement margin
- Client deadlines don't move just because the underlying data is a mess
- Every engagement re-solves the same classification problem from scratch, with nothing reusable carried forward
- A general AI tool guesses at supplier matches and invents things that aren't there, creating rework instead of saving time
Your taxonomy, our pipeline
Pearstop plugs into your delivery model as the classification layer underneath it, not a replacement for your team's analysis.
Bring your own taxonomy
Most consultancies sell their own category framework as part of their IP. Pearstop classifies against your taxonomy, or UNSPSC if you prefer, not the other way around.
Classify with guardrails
Deterministic matching first, cross-checked with an LLM, with every uncertain match flagged for review rather than guessed at, so the output can go straight into a client deliverable.
Deliver under your name
Output is white-labelled. Your client sees your firm's deliverable, not a third-party tool.
What this changes for a consultancy
Protect engagement margin
Take on more engagements without more headcount
A predictable cost line
How does an AI classification pipeline change a procurement consultancy's delivery model?
Spend classification is the slowest, least differentiated part of most category-management, sourcing-savings, and spend-cube engagements, and it's typically done by junior analysts or an offshore team working through a client's spend data line by line. An AI classification pipeline takes over that layer: matching and standardising supplier names, classifying line items against a taxonomy, whether that's UNSPSC or the consultancy's own proprietary framework, and flagging anything uncertain for human review rather than guessing. The output is white-labelled, so it slots into an existing delivery model as the data layer underneath it, freeing analyst time for the sourcing strategy and recommendations clients are actually paying the consultancy for.
Frequently asked
Can the output use our own taxonomy instead of UNSPSC?
Yes. Pearstop classifies against whatever taxonomy you specify, including a proprietary framework you sell as part of your own IP. UNSPSC is available if you'd rather use a standard one.
Is this white-labelled?
Yes. The deliverable is built to sit inside your own engagement output, not branded as a third-party tool.
How does pricing work across multiple client engagements?
Pricing is structured around volume and engagement cadence rather than a single flat fee, so it can be built into how you price your own client work upfront.
Want to see it on a real client dataset?
Talk to sales and we'll show you what your next engagement's spend data looks like after a first pass.
