AI & Deep Learning for Commerce
AI that sells. Not AI that demos well.
AI systems that reduce stockouts, increase AOV, detect fraud in real-time, and personalize at scale — integrated into the commerce platforms you already run.
What we solve
AI built into your commerce stack — not bolted on.
Six places where AI moves real numbers. These are the production patterns we see most often in retail AI work.
01Inventory
Demand forecasting
Predict stockouts before they happen. ML models trained on your sales history, seasonality, and external signals — feeding the procurement call directly.
- Daily-refresh forecasts
- Per-SKU, per-region, per-channel granularity
- Asymmetric loss functions for perishables
02Discovery
Intelligent search
Understand intent, handle misspellings, learn from queries. Vector search + NLU that surface the right products instead of keyword matches.
- Semantic + lexical hybrid ranking
- Embeddings tuned on your catalog
- Click-feedback loops for self-improvement
03Pricing
Dynamic pricing
Competitor monitoring + demand signals + margin optimization. Real-time pricing intelligence that maximizes revenue without eroding brand perception.
- Competitive scrape + reconciliation
- Price elasticity per SKU class
- Brand-floor guardrails enforced
04Risk
Fraud detection
Real-time ML scoring on every transaction. Catch fraud before it clears while minimizing false declines that cost legitimate revenue.
- Sub-100ms scoring latency
- Drift monitoring on production traffic
- Configurable approve / review / deny thresholds
05Experience
Personalization engine
Behavioral intelligence — not 'customers also bought.' Deep learning models that understand individual shopping journeys and predict next actions.
- Session-aware recommendations
- Cold-start handling for new visitors
- A/B harness baked in
06Automation
AI agents
Autonomous customer support, order management, and inventory optimization. Agents that act, escalate when needed, and learn continuously.
- Tool-using agents with audit logs
- Human-in-the-loop escalation paths
- Cost + latency observability per call
Stack deep dive: LLM & GenAI Consulting
The inference loop we build
Your signals in. Cited outcomes out.
Skip the hype. Here is the actual inference flow we deploy — the same model that turns sales velocity, inventory, and external signals into decisions your P&L can feel.
Illustrative — schematic of the model pipeline, not a performance claim.
SIGNALS IN
Sales velocity · seasonality · inventory · marketing · external (weather, social)
DESTM AI MODEL
Trained on YOUR data, not generic datasets — cites every output
DECISIONS OUT
Demand forecast · pricing · search ranking · fraud score — each with confidence + citation
Dynamic pricing
Margin protected on volume + competitor moves
Fraud detection
Fewer false declines, fraud caught earlier
Our process
The Destm Acceleration Framework.
Same 4-phase methodology that runs every Destm engagement — applied to AI work.
Diagnose
01Audit your data, the bottleneck, and the highest-ROI AI surface. Specific findings, not generic assessments.
Prioritized opportunity mapDesign
02Architecture, data flow, model contract, integration map. Validated with a working prototype, not a deck.
Spec + clickable prototypeDeliver
03Agile sprints. Working model in staging every two weeks. Senior engineers in your standup.
Production model + integrationEvolve
04Drift monitoring, model retraining, performance tuning, feature iteration. The system gets smarter every month.
Monthly performance reportsAI guardrails
Models ship with brakes, not just an engine.
Operational safety is built in, not bolted on. Every model and agent runs inside these layers before it touches a customer or a write.
Human-in-the-loop
Actions above a confidence threshold pause for a person. You set the threshold and raise it as trust earns it.
Evaluation suites
Accuracy, drift, and bias checks run on every change. A regression fails the eval before it ships.
Prompt & version control
Prompts and model versions are pinned and reviewed — no silent swaps, full rollback.
Audit logs
Every input, decision, and output is recorded. You can replay exactly why a model did what it did.
Data permissions
Models reach data through scoped, permissioned access — never the whole warehouse by default.
Fallback paths
When confidence is low or a service is down, the workflow degrades to a safe default, not a guess.
Once a model is live, drift and accuracy are watched in MetriQ — so a quiet degradation pages the team instead of eroding the numbers for a quarter. See the same guardrails running live on AI agents.
Across the work
AI in production
Trained
on your data, not generic sets
Embedded
in your existing platform
Monitored
for drift, not just deployed