Algorithm Development
Custom scoring, matching, and prediction algorithms. Multi-dimensional weighted scoring with confidence indicators and full explainability. Deployed as APIs, owned by you.
What is Algorithm Development?
Algorithm development means building scoring, matching, or prediction logic specific to your business problem. Not a generic SaaS tool that guesses based on industry averages. A system designed around your data structures, your business rules, and your definition of what a good outcome looks like.
A typical algorithm we build computes an overall score from multiple weighted dimensions. For a matching algorithm, that might be feature match (30%), tag similarity (25%), specification match (25%), context relevance (10%), and base quality (10%). Each dimension has its own scoring function. Feature matching uses fuzzy string comparison with Levenshtein distance. Tag similarity uses Jaccard coefficients. Spec matching detects criteria from free-text input and maps them to structured data. Every score comes with a confidence indicator and a full breakdown explaining what contributed to the result.
We also build the explainability layer. Every prediction includes a machine-readable breakdown (component scores with weights and descriptions), a human-readable summary, and a recommendation label. Your team and your users always understand why the algorithm produced a given result.
The finished algorithm is deployed as a typed service class with dependency injection. Scoring weights, stop words, and thresholds are all configurable through the constructor. Swap them in tests, tune them in production, or let different customers use different configurations. Deployed as a REST API with batch processing support for high-volume workloads.
Common Applications
What we build
Proprietary algorithms designed for specific business problems, not generic platforms.
Multi-Dimensional Scoring
Weighted scoring algorithms that evaluate items across 5+ dimensions simultaneously. Each dimension gets its own scoring function, configurable weights, and a confidence indicator. Results include full breakdowns so users understand exactly why something scored high or low.
Matching & Recommendation
Algorithms that match profiles to catalogs using feature matching, Jaccard similarity on tags, semantic spec detection from free-text, and context keyword analysis. Fuzzy matching with Levenshtein distance catches near-matches that exact string comparison misses.
Demand Forecasting
Time-series models (Prophet, LightGBM) that predict demand at SKU or location level. Feature engineering from seasonality, promotions, and external signals. Integrate directly with your inventory and planning systems via REST API.
Confidence & Explainability
Every algorithm we build includes a confidence service that tells your users how reliable the output is. We calculate confidence from profile completeness, data quality, and evidence coverage, then surface it as a 4-level indicator with raw signals for transparency.
Churn Prediction
Classification models that identify at-risk customers before they leave. Feature engineering from usage patterns, support tickets, and billing data. Deployed as batch scoring jobs or real-time API endpoints with sub-200ms response times.
Score Explanation
Machine-readable and human-readable explanations for every prediction. Component breakdowns showing which dimensions contributed most, feature analysis listing what matched and what is missing, and recommendation labels (strong match, has gaps, not recommended).
Our Stack
How we build it
Scoring & Matching
- →Multi-dimensional weighted scoring with configurable weights per dimension
- →Jaccard similarity for set-based comparisons (tags, features, categories)
- →Levenshtein distance fuzzy matching for text that is close but not exact
- →Semantic spec detection from free-text mapped to structured product data
- →pgvector embeddings for semantic similarity search across large catalogs
Confidence & Explainability
- →4-level confidence indicators based on data completeness, evidence quality, and algorithm certainty
- →Component-level score breakdowns with weights and human-readable descriptions
- →Feature analysis: what matched, what is missing, what triggered deal-breaker penalties
- →Structured recommendation labels for downstream logic (strong match, has gaps, not recommended)
Architecture
- →Class-based services with constructor dependency injection for testability
- →Batch processing for scoring hundreds of items in a single call
- →Deployed as REST APIs with typed request/response schemas
- →PostgreSQL + Supabase for data storage, Redis for caching hot paths
Validation
Every algorithm gets a corresponding test suite. We validate scoring logic against known input/output pairs, fuzz edge cases (empty data, null fields, oversized inputs), and benchmark performance against baseline models. Confidence calculations are tested independently from scoring logic. You see exactly how the algorithm performs before it touches production data.
Is This Right For You?
Ideal client profile
Algorithm development is the right fit if you:
- →Have 12+ months of historical data relevant to the problem
- →Need predictions specific to your business, not industry averages
- →Want the model integrated into your existing workflow tools
- →Require full code ownership with no vendor lock-in
- →Have outgrown the built-in scoring or forecasting in your CRM/ERP
Flexible Engagement
Every project is scoped to your data, complexity, and integration needs. We offer both project-based builds and monthly retainers for ongoing model management. Book a discovery call and we will scope it together.
Ready to build a custom algorithm?
Book a discovery call. We will assess your data, define the problem, and scope a model that integrates with your existing systems.
Also see our API Development services for custom integrations, or browse all solutions.