AI that ships and stays accurate.

    LLM integrations, custom ML models, and data pipelines — built for production, not proof-of-concept decks.

    Overview

    We don't build AI demos. We build AI features that stay in production — with monitoring, fallback logic, and confidence thresholds so your product doesn't embarrass itself on edge cases.

    The most common mistake we see: companies build an ML model without a plan for retraining it. We build the full loop — data pipelines, model versioning, deployment, and the monitoring layer that tells you when accuracy drifts.

    Practical applications: LLM-powered support that routes to humans when needed, recommendation engines tuned on your actual purchase data, fraud detection that updates weekly not annually.

    AI & ML Capabilities

    What we build and the tools we use to build it

    Custom AI Models

    Build and train models tailored to your specific data and business requirements using PyTorch and TensorFlow.

    LLM Integration

    Integrate GPT-4, Claude, and other LLMs for intelligent features, semantic search, and content generation.

    Predictive Analytics

    Forecast trends, predict customer behavior, and optimize business operations with ML-powered insights.

    Data Engineering

    Build reliable data pipelines, warehouses, and ETL processes for ML-ready datasets.

    MLOps & Deployment

    Production-grade model deployment, monitoring, versioning, and continuous retraining pipelines.

    Recommendation Systems

    Personalized content and product recommendations that boost engagement and conversions.

    How we work

    Four phases. Fixed scope. You see working software every week.

    01

    Discovery

    We map what you actually need — not what sounds right in a doc. Scope, dependencies, and a week-by-week plan agreed before we write a line of code.

    02

    Design

    Architecture first. We spec the data model, API contracts, and component structure. Changes here cost hours — changes in week three cost weeks.

    03

    Development

    Weekly deploys to staging. You see working software every 7 days, not at the end of the project. If something's off, you catch it early.

    04

    Delivery

    We ship, then we document. You get production access, runbooks, and a handoff call. Support retainers available if you want us to stay involved.

    You have direct access to the engineer building your product — not an account manager. Weekly demos, async Slack channel, fixed price. No surprises on the invoice.

    Why Choose AlgoSmiths

    What's different about working with us

    Business-First Approach

    We focus on AI applications that deliver measurable ROI, not just impressive technology demos.

    Production-Ready ML

    Our ML systems are built for real-world usage with monitoring, fallbacks, and continuous improvement.

    Data Scientists & Engineers

    Cross-functional team combining ML expertise with software engineering best practices.

    AI Solutions at Scale

    We've built AI systems handling 1M+ requests daily with sub-second response times.

    Ready to ship? Let's scope it on a free call.

    You talk directly to the engineer who'll build it. Fixed price, honest scope — on the first call. No pitch deck, no account manager.

    Frequently Asked Questions

    Common questions about this service

    Not always. While custom models require substantial data, we can use transfer learning, pre-trained models, and LLM APIs to build valuable AI features even with limited data. We assess your use case and recommend the most practical approach.

    We implement rigorous testing, cross-validation, A/B testing in production, and continuous monitoring. All models include confidence thresholds and fallback mechanisms to ensure reliable behavior even in edge cases.

    We add AI capabilities to existing systems via APIs, microservices, or embedded models. Integration is API-based — no changes to your current app required.

    Simple integrations (like adding GPT-4 features) can be done in 2-4 weeks. Custom ML models typically take 2-4 months including data preparation, model training, evaluation, and production deployment.

    Guides from our engineers

    Practical write-ups on the problems this work solves, from the AlgoSmiths blog.

    All articles on the blog