Varanasi, UP- IN
AI Development
Production-grade AI features, agents, and copilots—with measurable accuracy, safety, and cost control.
AI Development
We design and ship AI that does real work: retrieval-augmented assistants, task-oriented agents, document understanding, voice/vision pipelines, and multilingual experiences. The focus is reliability over theatrics—tight latency, consistent outputs, and guardrails that respect your risk posture. We build on best-fit models (hosted or open-weight), wire clean data contracts, and deliver interfaces your teams and customers actually use.
Under the hood: robust retrieval (vector search, chunking, re-ranking), policy-aware prompting, evaluation harnesses, and observability for accuracy, hallucination rate, TTFT, throughput, and cost per task. Virtunetic accelerates content, localization, and knowledge-base generation.
Architecture, Data, and Safety by Design
We start with domain modeling—entities, workflows, and constraints—then choose the right architecture (RAG for proprietary context, function/tool calling for actions, fine-tuning or adapters when needed). Data pipelines handle PII redaction, normalization, and lineage; prompts and policies live in a versioned registry. Safety isn’t a bolt-on: jailbreak tests, content filters, allow/deny lists, rate limiting, and human-in-the-loop review where risk is high. Compliance and privacy are embedded (data minimization, encryption, retention policies, and access controls) with mapping to common frameworks and laws, including India’s DPDP Act and GDPR.
Ship, Measure, and Operate at Scale
We treat AI like any other critical system: CI/CD for prompts and models, canary releases, feature flags, autoscaling, and graceful degradation when providers wobble. Telemetry tracks accuracy against golden sets, containment and deflection rates, TTFT and p95 latency, cost per 1K tokens/task, and user satisfaction—so decisions are empirical. Cost governance uses semantic caching, prompt compression, truncation, and selective model routing; reliability comes from fallbacks and replayable workflows. Your teams get dashboards, runbooks, and SLAs so operating AI becomes predictable, not mysterious.
Frequently Asked Questions
Discovery and data prep usually take 1–2 weeks; an MVP lands in ~4–8 weeks depending on scope, integrations, and evaluation depth.
Whichever best fits the job: hosted foundation models for speed, or open-weight models in your VPC for control and data residency.
No—by default we prevent vendor-side training, apply data-minimization, and can keep all processing inside your cloud boundary.
Task-specific golden sets, automatic grading with clear rubrics, LLM-as-judge with calibrations, and human review for high-risk outputs.
Yes—identity/SSO, CRM/ERP, ticketing, analytics, data lakes, and messaging. Outputs are structured so downstream systems can act.
Semantic caching, smart truncation, tool calling over long prompts, adaptive model routing, streaming responses, and autoscaling with budgets.
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