Services

Governance that is enforced in the platform, not written in a policy document. A fixed-scope plan that answers who owns the AI controls, where models run, how every call is routed and capped, what guardrails apply, what each agent may touch in production, and what record it leaves. The team that writes it is the team that builds it out.

An LLM application architecture audit measures how your LLM system behaves in production: where tokens and money leak, where retrieval fails, which calls should never reach the expensive model, and what your agents are allowed to touch. Allerin runs it on your real usage exports in two to three weeks, for a fixed $15,000 to $25,000, and ends with a prioritized fix plan your team can carry out.

A two-week lab that turns a risky idea into a user-tested prototype and a technical spike, ending in a clear build, park, or pivot call.

Taking an AI proof of concept to production means rebuilding it to run on live data, inside the systems your business already uses, with tests, monitoring and a rollback. Allerin does this as a fixed-price engagement: production architecture first, shadow mode beside your current system, then 5%, 25% and all traffic, each step gated by the same evaluation set. Typically 8 to 16 weeks and $80,000 to $150,000 for one workflow.

A dedicated cross-functional pod that owns a slice of your roadmap and ships on a predictable cadence, with transparent velocity and quality metrics.

KPI and GIS dashboards on a governed warehouse, with data-quality checks and alerting so the numbers your team acts on hold up.

Takes a computer-vision model from proof-of-concept to production on edge or cloud, with measured precision, low-latency inference, and drift monitoring.

A build track that ships a production RAG feature grounded in your own data, with accuracy evals, safety gates, and cost-per-query tracking.

Multi-agent systems that carry out back-office decisions under policy guardrails, with a human reviewer on the exceptions and an audit trail on every action.

Your edge and cloud infrastructure as versioned code, with CI/CD that rolls back in minutes, tracing across edge and origin, and hard spend caps on platforms that ship without them.

One control plane to route, run, observe, and govern multi-model and multi-agent apps, with cost policies, guardrails, and audit trails.

Security hardening built for AI failure modes (prompt injection, model theft, data poisoning), mapped to SOC 2, NIST AI RMF, HIPAA, and CJIS with audit-ready evidence.

Model registration, quality evaluation and rollback that preserve compatible models, features and serving configuration. Inspect a runnable synthetic demonstration before defining your production acceptance tests.

Rails consultancy since 2005: upgrades to Rails 8.1, new product builds, forward-deployed Rails engineers who stay on your team, and rescue of Rails codebases other teams left broken. Every engagement opens with a review of the code. No monthly plans, no quote before the code is read.

Assessment-first upgrades to Rails 8.1 for applications on 4.2 through 8.0: one minor version per branch and deploy, Ruby stepped separately, a verdict for every gem, the front end scoped on its own.

Production-ready connectors between your systems, with contract tests, retry and idempotency logic, and dual-run reconciliation before cutover.

Modernizes a legacy platform without a feature freeze: modular boundaries first, then observability and cost controls, with dual-run validation for a zero-downtime cutover.