LLM apps, RAG knowledge assistants, and ML models that connect to your real systems and data.
Semantic search and question-answering over policies, contracts, SOPs, and knowledge bases.
Context-aware assistants embedded inside your existing tools and dashboards.
Demand forecasts, risk scoring, and alerting based on your data.
Better search, retrieval, and personalization in SaaS and ecommerce experiences.
Selecting models, designing prompts, tools, and interaction flows that actually work in production.
Ingestion, chunking, embeddings, vector stores, retrieval strategies, and evaluation harnesses.
Traditional ML where it fits — classification, regression, clustering, time series.
Eval suites, guardrails, fallbacks, and monitoring to keep outputs reliable.
AI features should feel native to the systems you already run. We design integrations to be secure, observable, and reversible.
AI features exposed as well-bounded services with auth, rate limits, and audit trails.
Connect to your databases, SaaS tools, file stores, and identity providers — not parallel copies.
Respect for tenancy, PII handling, redaction, and contractual data boundaries.
Clarify use cases, data sources, constraints, and what “good” looks like.
Build a focused proof of concept to validate value with real data.
Harden, integrate, add monitoring and MLOps. Ship to real users.
Refine based on feedback, evaluation results, and real-world performance.
Agentic AI that uses your tools and data to handle multi-step work — not just chat.
SoftwareModern web and platform builds with AI-powered features baked in from day one.
Cloud · OpsScalable, observable, and secure foundations for your applications and AI workloads.
A 30-minute scoping call gets you a practical answer — including whether AI is the right tool at all.
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