Enterprise Data & AI Engineering
Raw Records Into Real Decisions
We structure the ERP data your operations already generate, then automate the pipelines that keep it analysis-ready. Predictive models and live dashboards are built on that foundation, never on guesswork.
Live Data Ticker Panel
DATA & AI STRATEGY
Make Every Record Answerable
Your AI strategy starts with a cleansing pass, not a model. We deduplicate records, close schema gaps, and reconcile fields across systems before a single integration is designed.
We already know where your valuable data sits, because we build the ERPs that hold it. Running Odoo implementations means we read margin, movement, and demand signals at table level from day one.
You receive a connected system, not a static report. Queries hit live operational data, and answers surface inside the dashboard your team already opens each morning.
DATA & AI STRATEGY
Big Data Automation
- Automated ETL pipelines
- Multi-source system integration
- Scheduled validation checks
Automated
End The Manual Export Cycle
Pipelines pull from Odoo, storefronts, freight systems, and spreadsheets on a fixed schedule. Records are cleaned, matched, and written to a warehouse your analysts can query directly.
The return is time handed back to your team. Nobody spends the first week of the month rebuilding a dataset before anyone can read a number.
Scheduled
Pipelines run hourly, nightly, or on system events, with no human trigger required.
Multi-Source
ERP, e-commerce, freight, and third-party APIs land in one reconciled dataset.
Validated
Row counts, schema drift, and null thresholds are checked on every single run.
AI ANALYTICS
Operational Answers On Demand
Ask in plain language and receive an answer built from your own records. Predictive dashboards carry the recurring questions, while alerts push the urgent ones to the right person.
AI ANALYTICS ASSISTANT — DEMO
Which freight routes lost margin last quarter?
Four routes fell below target margin last quarter. [Route KHI–LHE] dropped to 6.2% against a 14% target, driven by a 19% rise in fuel and detention charges. A route-level breakdown is ready to export.
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Natural Language Queries
ERP-Native Data
Alert-Driven Delivery
ML DEVELOPMENT
Models Trained On Your History
We build custom models on your own transaction history: demand forecasting, anomaly detection, churn scoring, and recommendation engines. Each is measured against agreed evaluation metrics before it reaches production.
A model is only as strong as the pipeline feeding it. Where records are inconsistent or incomplete, we correct the pipeline first and put that recommendation in writing.
- Demand forecasting models
- Inventory anomaly detection
- Customer churn prediction
- Product recommendation engines
- Document classification models
- Text sentiment analysis
WHY TRUST APP AURA
Expertise Proven In Production
Domain Knowledge
Delivery Evidence
What We Do Not Claim
Odoo Schema Architecture
We design the sales, stock, and accounting structures our clients run on, so we read their data at field level.
High-Volume Transaction Handling
Our own platforms process transaction data daily, which is where indexing and query performance problems genuinely surface.
Freight Costing Data
Job costing and route data from live logistics deployments shapes how we model operational margin.
High-SKU Inventory Data
Reconciliation and expiry tracking in a production system taught us the data-quality failures high-SKU retail creates.
Live ERP Deployments
Odoo implementations for logistics clients run in production today, generating the structured data our analytics layer consumes.
Our Own SaaS Platform
We operate a proprietary retail inventory platform, carrying the same daily data burden we ask clients to manage.
Full Stack Ownership
One team owns the ERP schema, the pipeline, and the model, so no context is lost between layers.
Documented Data Isolation
Client data trains client models only, under an architecture that is written down and auditable.
AI Without Clean Data
We Do not Claim
If your records are inconsistent or scattered, we scope the pipeline first and say so before any model is quoted.
Accuracy Before Evaluation
We Do not Claim
We quote no accuracy figure on data we have not seen; metrics are agreed with you before training begins.
One Model Everywhere
We Do not Claim
Freight demand and retail churn are different problems, so no reused model is billed to you as custom work.
Partner-Driven Tool Choices
We Do not Claim
We recommend the stack that fits your infrastructure, with alternatives documented rather than the vendor margin that suits us.
Our data credibility comes from operating live systems every day, not from a certificate issued by a platform vendor.