AI Development
Company
India
CV Infotech builds production-grade AI applications, generative AI platforms, multi-model integrations, AI automation, and AI SEO solutions, for clients in the USA, UK, Australia and Canada.
2
Published product examples
3+
AI models integrated
5.0
Clutch rating
14+
Years active
AI development that works in production, not just in the demo
AI development is not the same as building a ChatGPT wrapper. Production AI applications require prompt engineering that produces consistent output at scale, model routing that selects the right AI provider for each task type, quality scoring infrastructure that catches failures before they reach users, and security architecture that handles sensitive data correctly. These are software engineering problems, not AI research problems, and they require the same disciplines as any other production software build.
CV Infotech developed UltimaBot and UltimaWriter. UltimaBot brings website chat, email and voice communication into one platform. UltimaWriter supports AI-assisted writing and publishing workflows. Explore these product capabilities to help describe the workflow you need; your data, integrations and acceptance criteria are scoped separately.
We will tell you when AI is not the right solution. If a simpler rules-based system, a search index, an existing SaaS tool, or one of the available AI app builder alternatives solves the problem, we say so. AI adds genuine value when the problem involves language understanding, content generation at scale, pattern recognition in unstructured data, or intelligent automation of complex multi-step workflows. We scope every AI project with an honest feasibility assessment before any code is written.
Multi-model architecture
We build model-agnostic AI systems. GPT-4o, Claude 3.5, and Gemini Pro are routed based on task type, cost, and latency, not preference. Swapping providers is a config change, not a rewrite.
Prompt engineering
Prompts are treated as code. Versioned, tested, deployed through a controlled process, and rolled back when output quality regresses. Not a one-time creative exercise.
AI security
Sensitive data is not sent to AI models without a defined policy. We configure providers to prevent training-data use. UK GDPR and CCPA requirements are addressed in discovery.
Reviewable product examples
Explore the published UltimaBot and UltimaWriter capabilities, then agree which workflow, integrations and acceptance checks are relevant to your project.
AI development services
From first AI integration to full AI platform development and long-term AI product maintenance, CV Infotech covers the full delivery lifecycle.
AI platform development
Full-stack AI product development, from architecture and model integration through to user interface, admin dashboard, and ongoing maintenance. Built on production-grade infrastructure with the same team throughout.
Learn moreGenerative AI integration
Add generative AI capabilities to an existing application. Content generation, summarisation, classification, intelligent search, recommendation engines, and structured data extraction, integrated into your existing tech stack and user interface.
Learn moreAI automation and workflows
Automate complex multi-step business processes using AI to handle the decision points that rules-based automation cannot. Task orchestration, document processing, intelligent routing, and approval workflows.
If the workflow starts with a phone call, explore AI voice agent development for a bounded call-intake pilot, human handoff and failure-path testing.
Learn moreAI chatbot development
Custom AI chatbots built on GPT-4o or Claude, not generic chatbot platforms. Domain-specific knowledge, custom conversation flows, escalation logic, and integration with your existing CRM or helpdesk.
Learn moreAI SEO services
Optimise your brand and content for AI search engines, ChatGPT, Perplexity, Gemini, and Google AI Overviews. Structured data, entity recognition, llms.txt, and content architecture designed for AI citation.
Learn moreAI audit and strategy
Not sure where AI fits in your business? We conduct a structured audit of your current processes and technology stack, identify the highest-value AI integration points, and produce a prioritised roadmap with honest effort and ROI estimates.
Learn morePlan for changes after the first release
Models, APIs and provider terms can change after launch. Agree which outputs need human review, how model changes will be evaluated, and who owns monitoring and maintenance. The release plan should record acceptance checks and fallback behavior for the workflow being built.
🇺🇸 USA
CCPA-compliant AI data handling. AWS us-east-1 for primary deployments. HIPAA considerations discussed in discovery for healthcare AI. OpenAI and Anthropic business API tiers configured to prevent training-data use.
🇬🇧 United Kingdom
UK GDPR compliance for AI integrations, data minimisation, purpose limitation, and privacy by design. AWS eu-west-2 (London) for UK-hosted AI workloads. UK AI Act developments monitored and incorporated.
🇦🇺 Australia
Privacy Act 1988 compliance for AU clients. AWS ap-southeast-2 (Sydney) for AU-hosted AI deployments. Laura Maher, our AU client: 'Communication is 10 out of 10. Barely notice the time difference.'
Not locked to one AI provider
We build model-agnostic architectures. When OpenAI releases a better model, we route to it in hours. When a client needs Claude for compliance reasons, we switch without rewriting the application layer.
Prompts treated as code
We version, test, and deploy prompts through a controlled engineering process. When a model update causes output regression, we diagnose and fix it, not guess and retry.
Security in the AI layer
AI introduces new attack surfaces: prompt injection, data leakage through model outputs, and uncontrolled generation. We design for these from the start, not after a security review flags them.
Honest feasibility, not AI hype
We will tell you when a simpler solution is the right answer. AI adds genuine value in specific scenarios, and creates unnecessary complexity in others. Our scoping process distinguishes between the two.
AI development process
Every AI project at CV Infotech follows a structured process, from honest feasibility assessment through to production launch and ongoing improvement.
Discovery and AI feasibility
Weeks 1–2We assess the problem honestly before proposing AI. Is this a genuine AI use case or does a simpler solution work better? We document the requirements, the data available, the output expectations, and the compliance constraints. The output is a clear scope with realistic timelines and a fixed price.
AI strategy and architecture
Weeks 2–4Model selection, prompt architecture, quality scoring criteria, data flow, security design, and infrastructure topology. The model-agnostic routing layer is designed before any integration code begins. For generative AI projects, the prompt contract, the expected input and output format for each AI call, is defined and agreed.
Proof of concept
Weeks 3–6A working proof of concept tests the core AI behaviour against real inputs before the full build begins. This is where prompt engineering produces the first stable prompt versions, quality scoring criteria are calibrated, and edge cases are surfaced. The POC is reviewed and approved before the main build sprint begins.
Integration build
Months 2–5The full AI integration is built, model routing, prompt versioning system, quality gate, application interface, admin controls, and monitoring. For platform builds, this phase also covers the surrounding SaaS infrastructure: authentication, multi-tenancy, billing, and API access.
Testing, safety, and review
Month 5–6End-to-end testing across real inputs. Adversarial testing for prompt injection and output manipulation. Security review of AI data flows. Performance profiling under realistic load. Compliance review, CCPA, UK GDPR, or Privacy Act as applicable. Client review and UAT sign-off.
Production launch and monitoring
OngoingAI in production requires active monitoring, model performance, output quality trends, cost per generation, latency, and error rates. CV Infotech sets up monitoring dashboards and continues to manage the AI layer as models are updated by providers. The same engineers who built the integration continue to own it.
AI development questions
Let's assess it honestly first.
Tell us who will use the AI feature, the task it should support, and the systems it must connect to. Describe the output a person will review and what should happen when the result is uncertain. We can use that context to discuss a bounded first workflow and its acceptance criteria. Please do not send confidential records or credentials through the enquiry form.