AI & Machine Learning Solutions
From proof-of-concept to production — we build AI systems that deliver measurable ROI.
Overview
Aadyora designs, builds, and operates production AI systems for enterprises that need more than another proof-of-concept. We span the full lifecycle — problem framing, data readiness, model selection, deployment, monitoring, and continuous improvement — with a bias toward measurable business outcomes over benchmark vanity metrics.
Our work covers classical ML for forecasting and anomaly detection, applied LLM systems using RAG and tool-use, multi-agent orchestration for autonomous workflows, and computer vision for document and operational intelligence. Every engagement ships with MLOps tooling, eval harnesses, and observability — so the system stays useful as the world changes.
We work alongside your engineering and data teams, not around them. Code, prompts, evals, and infrastructure-as-code are handed over as first-class artifacts you own and can extend.
What we deliver
LLM Applications & RAG Systems
Retrieval-augmented generation over your proprietary knowledge, tool-use orchestration, structured output extraction, and hallucination-resistant pipelines with eval coverage.
Agentic AI Development
Multi-agent systems that plan, execute, and self-correct across long-horizon enterprise workflows — built on production-grade frameworks with guardrails and human-in-the-loop checkpoints.
Predictive & Prescriptive ML
Demand forecasting, churn prediction, anomaly detection, recommendation systems, and optimization models trained and operated on your data.
Computer Vision & Document AI
Invoice and form extraction, quality inspection, OCR pipelines, and visual analytics for operations, retail, and industrial use cases.
MLOps & AI Platform Engineering
Model registries, feature stores, CI/CD for models, drift monitoring, evaluation pipelines, and cost-optimized inference — on your cloud, in your VPC.
AI Strategy & Readiness
Capability assessment, opportunity mapping, vendor and model selection, and roadmaps that sequence quick wins ahead of platform investments.
Outcomes you can expect
- Time-to-production for AI features reduced from quarters to weeks
- Inference cost cut 30–70% through model and prompt optimization
- Documented eval scores and regression tests for every shipped capability
- Human handoff rates measured and reduced over time, not assumed
How we engage
1. Discover
Workshop with stakeholders to identify high-value, feasible use cases. Audit data readiness, governance posture, and integration points.
2. Prototype
Build a vertical-slice prototype on real data within 2–4 weeks. Define eval criteria up front so success is measurable, not subjective.
3. Productionize
Harden the prototype: observability, evals, CI/CD, security review, cost controls, and rollback strategy. Deploy in your environment.
4. Operate & Improve
Monitor quality, cost, and adoption. Iterate on prompts, retrieval, and model choice as the underlying tech evolves.
Frequently asked questions
How is Aadyora different from a generic AI consultancy?
We build and operate, not just advise. Every engagement ends with production code, evals, and MLOps tooling you own — not a slide deck. We also lead with measurable outcomes (cost, latency, accuracy, adoption) rather than model benchmarks.
Which AI models and frameworks do you use?
We are model-agnostic. We work with frontier APIs (Anthropic Claude, OpenAI GPT, Google Gemini), open models (Llama, Mistral, Qwen), and classical ML stacks. Selection is driven by your data sensitivity, latency, cost, and accuracy requirements.
Can you deploy AI inside our VPC or on-premise?
Yes. We routinely deploy in customer-controlled environments using open models, private cloud endpoints (AWS Bedrock, Azure OpenAI, GCP Vertex), or fully on-premise inference for regulated industries.
How do you handle hallucinations and accuracy in LLM systems?
We design for it: grounded retrieval with citations, structured output validation, automated eval suites that run on every change, confidence thresholds with human handoff, and red-team prompts as part of CI.
What is the typical engagement size and timeline?
Prototype engagements run 4–8 weeks. Production rollouts run 3–6 months. Ongoing operate-and-improve retainers run 6+ months. We also do fixed-scope assessments in 2 weeks for buy-vs-build decisions.
Continue exploring
Ready to start?
Tell us about your problem — we’ll respond within one business day with a concrete next step.
Contact Aadyora