Claravance Intelligence builds production AI systems across agents, automation, conversational AI, data engineering, machine learning, and generative applications - integrated with your infrastructure and designed with evaluation, monitoring, and governance.
AI Agents
- AI Agents & Agentic Workflows
Conversational AI
- Chatbots and chatbot services
- Virtual assistants and conversational AI
Generative AI
- Generative AI solutions for content, knowledge, and productivity
Data & ML
- Data-pipeline architecture
- Custom machine learning model engineering
AI Integration
- Secure integration with Azure OpenAI, Claude, and your existing stack
Who is it for?
Organizations with specific, high-value processes where a generic tool will not fit.
Talk to intelligenceWhy Claravance
One partner rather than five vendors, AI-native from the first engagement, staffed by senior practitioners, and delivered across the US and India.
End-to-end
Strategy through execution, all under one roof. One accountable partner and one contract instead of five vendors to coordinate.
AI-native
We do not bolt AI onto legacy services. Every engagement uses agentic AI, automation, and intelligent tooling from the start.
Seniority
Engagements are staffed by practitioners who have delivered at enterprise scale, so there is seniority on the problem from day one.
Global scale
US leadership and client engagement with India-led engineering, for enterprise-grade quality and competitive economics.
The industry has a pilot problem.
Most AI initiatives fail between the demo and daily operation because of integration, data quality, evaluation, or trust.
We design around real workflows and connect AI to your CRM, ERP, ticketing, data, and identity systems.
An AI system that can't touch your systems of record is a very expensive suggestion box.
What We Build
Agentic AI & Automation
We build agents that complete bounded work across onboarding, claims, orders, documents, and service desks, escalating to people whenever confidence or permissions require it.
Conversational AI & Chatbots
We build grounded assistants for customer support, employee self-service, and product guidance that answer from approved knowledge and hand exceptions to people.
Data Engineering & ML
We build pipelines, warehouses, feature infrastructure, and machine-learning systems for forecasting, anomaly detection, recommendations, classification, and scoring, supported by production MLOps and drift monitoring.
Generative AI Applications
We build model-agnostic applications for drafting, summarization, structured extraction, controlled content generation, and multimodal workflows, allowing models to change without rebuilding the product.
How We De-Risk AI Builds
01
Map & Prototype the Workflow
We map decisions and systems, then test feasibility against real data before committing to a build.
02
Evaluate Like It Matters
Accuracy, safety, latency, and cost are measured before launch and continuously after.
03
Govern From Day One
Access, audit logs, policy alignment, and AI-specific security controls are built in.
04
Hand Over or Host
We transfer the system with training and documentation, or operate it under managed services.
Not sure where AI fits? Start with Advisory before committing to a build.
Frequently Asked Questions
Custom AI development is the design, build, integration, and operation of AI systems tailored to your workflows and connected to your systems of record - as opposed to configuring an off-the-shelf tool. It spans agents, conversational AI, ML models, and generative AI applications.
A chatbot answers questions; an agent completes tasks. Agentic AI can plan multi-step work, call your systems' APIs, take permitted actions, and escalate to humans - a chatbot's job ends at the reply. Many real systems combine both: conversation in front, agents behind.
A well-scoped first system typically reaches production in eight to sixteen weeks: two to four for discovery and design, the remainder for build, integration, evaluation, and controlled rollout. Timelines stretch when data readiness gaps surface - which is why we test data early.
Whichever fits the use case on quality, latency, cost, and data-residency grounds. We architect model-agnostic systems so you can change providers as models improve - the model layer is the most volatile part of the stack and your architecture should assume that.
By grounding answers in retrieved company sources with citations, constraining the system's scope, adding evaluation gates before launch, monitoring quality continuously, and routing low-confidence cases to humans. No serious vendor promises zero errors - the discipline is detection, containment, and escalation.
No. We architect deployments - API-based with contractual no-training terms, private cloud, or self-hosted open-weight models - so your data stays inside your compliance boundary. Data-flow design is part of every engagement's security review.
Scope Your First System
Bring a time-consuming workflow and learn whether AI can own it, what it requires, and what it is worth.