P(Success | Data)
Abstract AI prototypes do not equal production systems. Before LineEquation writes a single line of production code, we run a structured evaluation that answers the only question that matters: given your data, your infrastructure, and your business constraints, can we build a system that delivers measurable commercial value?
This is not a perfunctory due diligence exercise. Our evaluation phase is a hands-on technical audit led by senior data scientists and ML engineers who have seen enough failed projects to know exactly where they break. We profile your data for quality, coverage, and bias. We identify the class of algorithms that match your latency, accuracy, and interpretability requirements. And we model the expected ROI against the engineering investment required — so your leadership team can make an informed go/no-go decision backed by evidence, not vendor promises.
Evaluation Methodology
Data Architecture Assessment
We evaluate the cleanliness, scale, and accessibility of your enterprise data — warehouses, lakes, operational databases, SaaS application exports, and external feeds. Our assessment covers schema consistency, null rates, cardinality distributions, temporal coverage gaps, labelling quality (for supervised tasks), and the engineering effort required to get raw data into a model-ready state. We use profiling tools (Great Expectations, dbt tests, custom statistical checks) and produce a data readiness scorecard that quantifies risk at the source level.
Algorithmic Feasibility
Not every problem needs deep learning, and not every problem can be solved with a gradient-boosted tree. We map your use case against the landscape of available approaches — statistical models, classical ML, deep learning, large language models, rule-based systems, or hybrid architectures — and identify the approach that best fits your risk profile, latency constraints, interpretability requirements, and data volume. Where possible, we run rapid prototyping experiments (2–4 week sprints) on a representative data sample to validate feasibility before committing to a full build.
Commercial Viability & ROI Modelling
We align AI outputs strictly with commercial outcomes — reducing operational costs, increasing revenue, improving decision speed, or unlocking entirely new capabilities. Our viability assessment includes a cost model (compute, engineering, ongoing maintenance), a benefit model (quantified impact on the target KPI), and a sensitivity analysis that shows how performance degrades under realistic scenarios. The deliverable is a decision-grade business case, not a slide deck.
Risk & Governance Assessment
We evaluate regulatory, ethical, and operational risks before engineering begins. This includes bias auditing of training data, fairness metric selection, model interpretability requirements (SHAP, LIME, attention visualisation), data privacy compliance (GDPR, CCPA, HIPAA), and operational risk scenarios — what happens when the model is wrong, when data quality degrades, or when upstream systems change without notice.
Enterprise AI Feasibility Estimator
Estimate your AI workload feasibility and recommended architecture based on your enterprise data scale and operational latency requirements.