Softment

AI Development

AI Recommendation System Development

We build recommendation systems that improve engagement and conversion: embeddings, ranking, feedback loops, and evaluation—integrated into your product with production-minded reliability.

TimelineTypical: 4–10 weeks (scope-dependent)
Starting at$2.5k
Security-first AI integrations • Evals + logging + guardrails included

Overview

What this service is

This service designs and implements a recommendation system tailored to your product: data model, candidate generation, ranking logic, and serving strategy.

We create an evaluation approach (offline metrics plus rollout strategy) so improvements are measured instead of guessed.

Delivery includes integration into your app/API, monitoring, and guidance for iterating as data grows and user behaviour changes.

Benefits

What you get

Higher engagement and retention

Show users more relevant items so they return and complete more actions.

Improved conversion

Better ranking can increase purchases, signups, and content consumption.

Measurable outcomes

Evaluation and rollout plans so you can quantify lift and avoid wasted work.

Cold-start strategies

Fallback logic for new users or new items to avoid empty recommendations.

Production integration

APIs and data pipelines built to run reliably, not just in notebooks.

Long-term iteration path

Feedback loops and monitoring so quality improves as your dataset grows.

Features

What we deliver

Data modelling + event tracking plan

Define the events and entities needed to train and evaluate recommendations reliably.

Candidate generation

Embeddings and similarity search for generating relevant candidate items at scale.

Ranking strategy

Ranking models or heuristics aligned to your business goal (CTR, purchase, retention).

Offline evaluation harness

Metrics and test sets that let you validate improvements before shipping broadly.

Serving API integration

Real-time or near-real-time recommendation endpoints integrated into your product stack.

Monitoring + drift checks

Observability hooks so you can detect quality regressions and update strategy over time.

Process

How we work

1
4–7 days

Discovery

We confirm goals, data availability, and constraints—then define the first recommendation milestone.

2
1–2 weeks

Data setup

We map entities/events and prepare training and evaluation datasets for experimentation.

3
2–6 weeks

Build

We implement candidate generation, ranking, and serving endpoints with integration into your product.

4
1–2 weeks

Evaluation

We validate offline metrics and prepare rollout experiments to measure lift safely.

5
3–5 days

Launch + iteration

We ship monitoring and a roadmap for improving quality as data and usage grow.

Tech Stack

Technologies we use

Core

EmbeddingsVector search (pgvector/Pinecone)Ranking strategiesPostgreSQL / data warehouse

Tools

Python / Node integrationFeature pipelinesBatch + streaming jobsEvaluation metrics

Services

API servingMonitoring

Use Cases

Who this is for

E-commerce product recommendations

Related products, frequently bought together, and personalised ranking across categories.

Content and feed ranking

Recommend articles, videos, or posts based on user history and content similarity.

Marketplace discovery

Rank listings and suggestions based on behaviour, inventory signals, and relevance.

B2B resource recommendations

Suggest documents, templates, or workflows inside enterprise products based on usage patterns.

Personalised onboarding

Guide users to relevant actions and content in their first session to reduce drop-off.

FAQ

Frequently asked questions

Not necessarily. We can start with embeddings and heuristics, then evolve to stronger ranking as data grows and tracking improves.

We define a primary metric (CTR, conversion, retention) and implement offline evaluation plus rollout experiments where possible.

Yes. Serving strategy depends on your needs—some systems are batch-based, others are near-real-time with caching for speed.

Yes. We design fallback logic (popular items, onboarding signals) so recommendations are useful from session one.

Yes. We can integrate recommendation endpoints and pipelines with your existing data stack and APIs.

Regional

Delivery considerations for your region

Compliance & Data (US)

For US teams, we build with auditability in mind: clear access boundaries, least-privilege roles, and reviewable operational controls.

We can align delivery with SOC 2 / ISO-friendly practices (without claiming certification): evidence-ready logs, secure-by-default config, and clear ownership.

  • SOC 2 / ISO-friendly implementation patterns (no certification claims)
  • Least-privilege access and permission boundaries
  • Security review checklists for auth, payments, and data flows
  • PII-safe logging + incident response playbooks (on request)
  • Retention and deletion flows where required
  • NDA + vendor onboarding docs on request

Timezone & Collaboration (Americas)

We support teams across the Americas with meeting windows that work for EST/CST/MST/PST.

We keep delivery predictable with weekly milestones, concise async updates, and written decisions to reduce calendar load.

  • Americas overlap with EST/PST-friendly windows
  • Async-first updates with written decisions
  • Weekly milestone demos + change control
  • Fast turnaround on blockers and clarifications
  • Clear owner per workstream and escalation path

Engagement & Procurement (US)

US-friendly engagement structure: clear SOWs, milestone billing, and invoice cadence that fits typical procurement workflows.

If you need vendor onboarding artefacts, we can provide security posture summaries and delivery process documentation.

  • USD invoicing and milestone-based payment schedules
  • SOW + scope lock options for fixed-scope work
  • Time-and-materials for evolving requirements
  • Procurement-ready documentation on request
  • Optional paid discovery to de-risk delivery

Security & Quality (US)

We ship with a security-first checklist and performance budgets—so releases stay stable under real traffic.

Expect clean PRs, reviewable changes, and production-ready testing from day one.

  • Threat-aware checks for auth, roles, and sensitive data flows
  • CI-friendly testing: unit + integration + critical path smoke tests
  • Performance budgets (Core Web Vitals-minded) and bundle checks
  • Structured logging + error tracking hooks (Sentry-ready)
  • Rollback-safe releases and clear release notes
Ready to start?

Want measurable personalisation outcomes?

Share your data sources and the actions you want to optimise. We’ll propose a recommendation approach and rollout plan.

Evaluation + monitoring included.