Solution to Manual Quotation Process
This is a project feature of automating quotation processes for an SKU-Heavy distributor.
In traditional distribution operations, sales administrators spend countless hours matching incoming customer enquiries to tens of thousands of SKUs. Enquiries often arrive in inconsistent formats, and product descriptions rarely match inventory names exactly. With over 15,000 items in stock, there could be multiple highly similar matches to a single inquiry item. Finding the right match can be slow, error-prone, and heavily dependent on experience and domain knowledge of a logic process to break down the request into parts to find the correct inventory.
As business volume grows in volume or SKU, the challenge compounds. Delays and human errors become more prevalent. Staff must scale linearly with enquiries, and onboarding new hires becomes a continuous bottleneck.
SKUMatch changes that. By automating the quotation process, it delivers faster, more accurate, and consistent responses — enabling companies to scale without proportional headcount growth. The system intelligently interprets enquiries, identifies the correct inventory items, checks stock availability, and drafts ready-to-send quotations.
What's the Secret Sauce?
At its core, the SKUMatch engine uses an autoencoder-based semantic matcher trained on historic quotation data. The encoder learns latent "meaning" representations of each SKU — attributes like size, material, category, and brand — allowing it to identify the most relevant matches even when descriptions differ. To handle special edge cases, SKUMatch augments the neural model with hybrid fuzzy logic matching, combining the flexibility of machine learning with rule-based precision.
The second secret sauce is the quality of training data. As we have heard this well-known phrase in Machine Learning - "Garbage In Garbage Out". The accuracy performance of the automatic quotation engine is tightly dependent on the quality of a structured training data. This is usually the biggest underrated component in any AI tasks, that ultimately determines whether it is operationally usable. The boring things done right, not the sexy new jargonic AI architectures, are the true knights.
Our turnkey service transforms years of past quotation records into ML-ready data — a foundation for continuous retraining and accuracy improvements post-deployment.
With over a decade experience on Machine Learning development and real operational deployment, we know how to plan a real applied AI/ML development for real use. Not the cute little vibe-coded weekend project.
Structured data used for training and evaluation of Autoencoder Network to automate quotation process
Business Impact
AI has reached the maturity for mass-level adoption and has started to replace repetitive labour. Companies that do not adopt the use of AI or automation will lose out to their competitors who do. Awareness of the use of AI is widespread, but it is not easy to find implementers who have done applied ML in real deployed operations.
With SKUMatch, distributors can expect:
- Up to 66% reduction in labor cost
- At least 20% increase in revenue from faster turnaround times and improved consistency
- Scalable operations without expanding headcount
- Staff time refocused on high-value tasks — reviewing and approving quotations rather than manually searching for matches
As automation becomes a competitive standard, SKUMatch ensures your organization stays ahead.
Reach Out
If you think this product can potentially be a game changer in your business, reach out to join our limited pilot implementation and experience the next generation of quotation automation.