Accorda Solutions
Turn Data Into Decisions That Move the Business.
Applied data science that solves real problems search, recommendations, classification, and prediction with results measured in business outcomes, not model metrics.
What we do
Data science only creates value when it changes a decision or improves an outcome. A model with impressive accuracy scores that nobody acts on is not a success. We start every engagement by working backwards from the business outcome: what behaviour needs to change, what decision needs to improve, and what prediction or recommendation would make that happen.
We have built data science systems that produced measurable, documented results. A recruiter engagement system at SEEK that increased engagement by 10%. A product recommendation engine that lifted order value by 25%. A semantic search system that fundamentally changed how candidates found jobs. These are not theoretical improvements they were measured in production against baselines.
We do not default to the most complex model or the trendiest technique. We use the right tool for the data you have and the problem you are solving. Sometimes that is a gradient boosted tree. Sometimes it is a fine-tuned embedding model. We focus on what works, what is interpretable, and what your team can maintain without needing a PhD to understand.
What you get
Measurable business lift
Every project is scoped against a business metric engagement, conversion, retention, revenue not just model accuracy. If we cannot measure it, we reframe the problem until we can.
Better search and discovery
Semantic search and ranking systems that understand intent, not just keywords so users find what they need faster and your conversion rates reflect it.
Personalisation at scale
Recommendation systems that surface the right product, content, or action for each user built to scale with your catalogue and your user base.
Predictive advantage
Models that tell you what is likely to happen next churn, demand, fraud, lead quality so you can act before the outcome is determined.
How we work
A structured approach that moves fast without skipping the steps that matter.
- 01
Problem framing
We translate the business problem into a data science problem. What are we predicting? For whom? Against what baseline? This step saves months of work on the wrong solution.
- 02
Data audit
We assess what data you have, what is reliable, what is missing, and what would need to be collected or engineered to make the problem solvable.
- 03
Modelling and experimentation
We build, evaluate, and iterate on candidate models using offline metrics tied directly to the business objective. We document every experiment so decisions are traceable.
- 04
Validation
Before production, we validate the model against held-out data, edge cases, and where possible online A/B tests that confirm the offline metrics translate to real business impact.
- 05
Production and handoff
We package and deploy the solution, build the monitoring needed to track its ongoing performance, and make sure your team understands how to maintain and evolve it.
Common questions
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