Stop reconciling records by hand. Connect spend, suppliers, items, and contracts so your team can compare purchases, see its buying power, and act with confidence.
Description → Structured attributesEvidence stays with every field
More than traditional spend analysis
Bad data? We fix it.
More than spend analysis. Our data agents fix the records behind it: resolve duplicates, enrich item specifications, connect supplier identities, and turn contract clauses into usable fields.
We do more than analyze the records you have. Agents correct inconsistent descriptions, add missing attributes, reconcile suppliers, and structure contract terms so your team can use the data.
Different names. The same supplier.
Bring naming variations together so the supplier picture is easier to trust. Flag possible matches for review rather than silently combining distinct businesses.
The output: a consistent supplier view, with the original records preserved.
Incoming recordsProposed structure
ACME IND. LTD.→Acme Industrial Ltd.
Acme Industrial Limited→Acme Industrial Ltd.
ACME SERVICES LLC→Keep separate · Review entity
01 / Item matching
Find price discrepancies across your item master.
ITEM RESOLUTION● Agent working
PLANT 01BRG SKF 6205-2RS25 × 52 × 15 mm
PLANT 02Bearing, sealed, 620525 mm bore · 52 mm OD · 15 mm width
Yes. A focused data problem is a valid starting point. The initial scope can be cleaning, enrichment, supplier normalization, classification, or a combination.
What data do we need to provide?
Start with the records you want to improve and the rules you want them to meet. Relevant item specifications, supplier identifiers, or a category taxonomy can help. We agree the required inputs and secure transfer method before work begins.
Will this overwrite our source system?
The output and review process are agreed as part of the scope. A file-based starting point does not require changes to your source system. Any write-back or integration needs separate agreement and appropriate permissions.
How do we measure improvement?
Use the measures that fit the problem: required-field completeness, validation errors, unresolved supplier matches, or classification coverage. Agree a baseline and review method before comparing the result.
Why can’t we build our own agents using ChatGPT or Claude?
You can. Modern language models are powerful building blocks, and many teams use them successfully for individual procurement tasks. The investment comes in making those agents dependable across real workflows: connecting fragmented data, teaching category-specific economics, integrating tools, checking outputs, managing permissions and maintaining the system as requirements change.
AgentrixAI brings those pieces together. Our agents work with shared commercial context, expert-developed category intelligence and the tools to carry work forward. Evidence, checks and approvals stay connected across tasks, so your team can inspect recommendations and control execution.
Building internally means owning that development and ongoing maintenance. AgentrixAI gives you a foundation you can put to work and adapt to your business, so your team can focus on procurement decisions and results.
Start with the data you have.
Tell us what is hard to use today. We’ll help define a focused scope and a useful outcome.
