AI Development

We Build Production-Ready AI for Your Business

Not a wrapper around someone else’s model and not a proof of concept that stalls at the demo. We design, ship and run AI systems on your data, inside your workflows, with the evaluation and guardrails that let you put them in front of customers.

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  • 16+ years engineering
  • 200+ in-house engineers
  • Secure, scalable AI solutions

Trusted By

750+ happy clients, including top Fortune 500 companies

Overview

What is AI Development?

AI development is the end-to-end process of designing, training, and deploying AI systems that automate decisions, generate insights, and solve complex business problems at scale: built around your data, your workflows, and your specific outcomes.

Built on modern AI architecture

LLMs, RAG pipelines, agentic systems, or custom ML models, the architecture is chosen for your use case, not retrofitted from a generic tool.

Integrated into your stack

Native integration with your ERP, CRM, data pipelines, and third-party APIs. No middleware hacks, and no disconnected AI bolted on as an afterthought.

Owned entirely by you

Full IP ownership of every model, pipeline, and codebase, with zero vendor lock-in, no recurring licence fees, and complete data sovereignty.

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What We Build

AI Development Services

We build all of it and make sure every system works together. As a trusted AI Development Company in India, Sphinx Solutions engineers bespoke AI solutions across every layer of your business.

01

Custom AI solution development

As a custom AI development company, we create tailored AI models, such as predictive models for finance or custom neural networks, to solve specific operational challenges.

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02

LLM application development

Generic LLM wrappers plateau quickly; we build scalable production apps with memory, tools, retrieval, and guardrails.

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03

Generative AI development

We develop generative AI systems for content generation, automation, images, code, and compliant optimised outputs.

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04

AI agent & agentic AI development

We architect autonomous AI agents that plan, execute multi-step tasks, and recover from failures using AutoGen and LangGraph frameworks.

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05

Machine learning model development

We manage data prep, features, training, evaluation, and deployment, ensuring business-specific intelligence tailored.

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06

RAG & knowledge base development

RAG systems delivering accurate, up-to-date AI answers using your data, with an end-to-end pipeline implementation complete.

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07

NLP & text analytics

We transform unstructured text using sentiment analysis, entity recognition, classification, multilingual processing, and custom models.

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08

AI integration & API development

We integrate AI into ERP, CRM, SaaS, and internal tools using high-performance APIs with clear, maintainable documentation for teams.

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What We Solve

AI Solutions

We first understand your operational challenge, then design a precise AI solution tailored to your workflows, data, and growth goals, built uniquely for you, not reused from generic or existing use cases.

We fine-tune foundation models on your proprietary data so the AI speaks your domain, your terminology, your processes, and your edge cases. The result is a model that outperforms generic alternatives on your specific tasks.

We engineer computer vision systems for image classification, object detection, visual inspection, facial recognition, and document OCR built on TensorFlow, PyTorch, and OpenCV, deployable at the edge or in the cloud.

For clients with strict data governance, compliance requirements, or security mandates, we deploy AI models entirely within your infrastructure, so no data leaves your environment. Full capability, zero cloud dependency.

Before full investment, we build fast, focused AI proofs of concept that validate your use case against real data in 4 to 6 weeks. You get a working technical demonstration and a clear decision framework for production build.

We add AI capabilities to your existing software without rebuilding it. Intelligent automation layers, predictive modules, and NLP interfaces integrated into legacy systems using a clean API architecture.

We design and implement vector database infrastructure (Pinecone, Weaviate, ChromaDB, Qdrant), powering semantic search, recommendation systems, and RAG pipelines with the retrieval performance production systems demand.

We identify the highest-impact automation opportunities in your workflows and build AI-powered automation solutions combining LLM reasoning, structured decision logic, and RPA tooling to eliminate manual processing at scale.

How We Work

Custom AI Development Process

Our AI development lifecycle follows a structured, outcome-driven methodology built for transparency, speed, and zero architectural surprises. Here’s what happens from your first conversation to your first production deployment, and everything after.

  1. Step 1

    Discovery & use case definition

    What we do: We audit your existing systems, map your operational workflows, identify integration dependencies, and produce a detailed Software Requirements Specification (SRS), the single source of truth your entire project builds from.

    What you get

    • A clear AI project scope and use case definition.
    • Technology recommendations with rationale.
    • Data readiness assessment and gap analysis.
    • AI development cost estimate and timeline.

  2. Step 2

    Data strategy & preparation

    What we do: We analyse your data sources, build scalable pipelines, label data, and set up robust infrastructure, because AI performance ultimately depends on the quality of its data.

    What you get

    • Data pipeline architecture documentation.
    • Data governance and compliance framework.
    • Labelled, cleaned, and structured training datasets.
    • Ongoing data ingestion strategy.

  3. Step 3

    Model selection & architecture design

    What we do: Our AI architects design the system blueprint, select ML methods, choose between RAG and fine-tuning, and build the infrastructure for the performance and privacy the system needs.

    What you get

    • Documented AI architecture blueprint.
    • RAG pipeline design or fine-tuning specification.
    • Model selection rationale (LLM vs custom ML vs hybrid).
    • Security and compliance architecture (GDPR, HIPAA, SOC 2).

  4. Step 4

    Development & integration

    What we do: Development happens in structured sprints: building AI models and applications, integrating via APIs, and deploying to staging with demo-ready builds throughout.

    What you get

    • Sprint demos with working AI functionality.
    • Full access to your project management board.
    • Regularly updated product backlog.

  5. Step 5

    Testing, evaluation & safety

    What we do: We run AI benchmarks, hallucination tests, bias audits, adversarial red-teaming, latency profiling, and OWASP LLM Top 10 scans, then validate ML models on held-out and production data.

    What you get

    • Model evaluation reports with accuracy and performance metrics.
    • Bias and safety audit results.
    • Zero critical failures before production deployment.

  6. Step 6

    Deployment & CI/CD pipeline setup

    What we do: We deploy your AI system on AWS Bedrock, Azure OpenAI, GCP Vertex AI, or on-premise, using containers, Kubernetes, and CI/CD pipelines for zero-downtime updates.

    What you get

    • Production-ready AI deployment.
    • Infrastructure-as-code documentation via Terraform.
    • Fully automated model release pipeline.
    • Rollback strategy for model incidents.

  7. Step 7

    Post-launch support, monitoring & optimisation

    What we do: We deliver L1: L3 support, continuous performance monitoring, periodic retraining as your data evolves, and a clear AI roadmap to keep accuracy, reliability, and business alignment on track.

    What you get

    • SLA-backed response times.
    • Model retraining and optimisation schedule.
    • Monthly AI performance health reports.
    • On-call AI Support Engineer.

Case Studies

I Development Projects That Delivered Measurable Results

Every solution we build is AI-ready by architecture, so your business grows smarter. We engineer software around the way your business, processes, integrations, and growth targets. We bring years of delivery precision to every project across the world.

The No Stress Impress app shown on a phone

NO STRESS IMPRESS

AI-powered student productivity and mental wellness platform

Problem

Students struggled to balance academics, deadlines, and mental well-being. Existing tools were too generic or lacked personalisation, causing low engagement, poor productivity, and ineffective stress management.

Challenge

To build an AI-driven platform that manages tasks and schedules while understanding user behaviour, stress patterns, and productivity gaps, and still feels simple, engaging, and personal to a student.

Solution

An AI-powered mobile platform with task planning, mood tracking, and behavioural insights, integrating smart recommendations and analytics to lift productivity, engagement, and consistency.

The Koras.ai encrypted email interface on a laptop

KORAS.AI

Zero-plugin AI email encryption platform

Problem

Email lacked simple encryption. Existing solutions required plugins, technical setup, or complex workflows, which made secure communication inaccessible and inconvenient for everyday users.

Challenge

To design an AI-powered encryption system that works directly inside existing email platforms (no installations, integrations, or training) while holding to strong security standards.

Solution

An AI-based encryption platform where users secure an email by adding brackets in the subject line, automating encryption, decryption, and key management without disrupting the workflow they already have.

The ProMarketer.ai campaign dashboard on a laptop

PROMARKETER.AI

AI-driven marketing automation and optimisation platform

Problem

Marketers struggled to manage campaigns across platforms, analyse performance data, and optimise ads in real time. Manual processes led to inefficiencies and missed growth opportunities.

Challenge

To create a centralised AI platform that automates campaign management, delivers actionable insights, and continuously optimises marketing performance across multiple channels.

Solution

An AI-powered marketing automation system with campaign tracking, audience segmentation, and real-time optimisation, plus performance dashboards and smart recommendations to maximise ROI.

How We Build It

Technology Stack

Every technology in our AI software development services stack is chosen for performance, scalability, and long-term maintainability. As an AI software engineering company, we select every model, framework, and piece of infrastructure based on your use case.

  • GPT
  • Claude Sonnet
  • Gemini
  • Llama
  • Mistral
  • Phi

Open source vs. Closed-Source

Open Source LLM vs. Closed Source LLM Comparison

Choosing the right large language model is one of the most important decisions in any AI project. The right choice depends on your priorities around data privacy, customisation, cost, and how quickly you want to deploy.

Factor Open Source LLMs Closed-Source LLMs
Popular Examples Llama, Mistral, DeepSeek, Falcon GPT-4o, Claude, Gemini
Customisation & Control Full access to model weights and extensive fine-tuning capabilities. Limited customisation through APIs and provider-supported tools.
Data Privacy & Security Can be deployed on private servers, offering maximum control over sensitive data. Data governance depends on the provider’s security and compliance policies.
Deployment Options Supports on-premise, private cloud, and hybrid deployments. Primarily available through managed cloud services.
Cost Structure Lower licensing costs, but requires infrastructure and maintenance investment. Pay-as-you-go or subscription-based pricing with minimal infrastructure management.
Performance & Innovation Strong performance with flexibility for domain-specific optimisation. Often provides cutting-edge capabilities and the latest AI advancements.
Scalability & Maintenance Requires internal expertise or a development partner to manage scaling and updates. Provider handles infrastructure, updates, and scalability automatically.
Best Use Cases Enterprises needing privacy, compliance, customisation, and AI ownership. Businesses seeking faster deployment, ease of use, and rapid AI adoption.

Sources: OpenAI enterprise privacy documentation; Meta, LLaMA research publications. Last updated: 5 June 2026

Quick Recommendation

Choose open-source LLMs

Open-source LLMs are ideal for organisations that prioritise control, privacy, and customisation.

Choose closed-source LLMs

Closed-source LLMs are better suited for businesses looking for faster implementation, lower operational complexity, and access to state-of-the-art AI capabilities.

Choose a hybrid approach

Many enterprises now adopt a hybrid approach to balance innovation, security, and cost efficiency.

Expert Insights

There is no single best large language model for every business. Open-source models offer greater control and customisation, while closed-source models provide faster deployment and advanced capabilities. The right choice depends on your goals, data privacy requirements, scalability needs, and long-term AI strategy.
Anand Mahajan CEO, Sphinx Solutions

How much does it cost?

AI Development Cost

Not sure how much your AI development will cost?

Our AI Cost Calculator makes it simple. Answer a few quick questions about your idea, integrations, and features to get a realistic estimate instantly.

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How We Engage

Engagement Models That Fit Your AI Project

As a leading AI app development company, we offer three fully transparent engagement models designed to build enterprise AI without surrendering control, IP ownership, or delivery accountability.

01

Fixed scope AI delivery

Fixed-scope, milestone-based billing, perfect for AI PoCs, ML projects, and RAG solutions. It sets clear deliverables, timelines, and costs, giving you complete budget certainty from start to finish.

02

Agile AI development

Billed per sprint hours with a reprioritisable backlog and monthly invoicing. Ideal for LLM apps, agentic AI systems, and evolving AI products shaped by user feedback and advancing model capabilities.

03

Dedicated AI engineering team

An AI team embedded in your workflow (engineers, scientists, and MLOps specialists), delivering senior-level work and helping you scale capability without building an in-house team from scratch.

Industries

AI Built for How Your Industry Actually Works

Why Choose Us

Client-Oriented. On-Time Delivery.

16+

Years of experience

Sphinx has completed 16 successful years in the industry, helping businesses with technology and digital transformation, including AI-powered solutions across major industry verticals.

16+

Industry experts

We have a dedicated and reliable team of AI engineers, ML scientists, data engineers, and MLOps specialists covering every role in the AI development lifecycle.

1500+

Solutions delivered

We have delivered more than 1,500 solutions through a results-driven approach, many to recurring clients who keep expanding their AI capabilities with us.

200+

In-house resources

We have a dedicated in-house team, experienced and skilled across AI development, software engineering, QA, DevOps, and cloud infrastructure.

750+

Happy clients

We are trusted by 750+ clients for best-in-class AI solutions at competitive rates, with the delivery accountability enterprise clients demand.

50M+

Users love our work

Our client-centric approach and high-calibre AI engineering, built on current models and frameworks, have made us a trusted partner for businesses building AI.

Highly Rated Across Top Platforms

Connect with us

Turn Your Ideas Into Reality. Let’s Connect and Create Something Groundbreaking.

  • USA
  • UK
  • UAE
  • India

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Frequently Asked
Questions

Everything you need to know about building AI with us. Can’t find your answer? Get in touch.

AI helps businesses work smarter by automating repetitive tasks, improving productivity, and reducing operational costs. It can enhance customer experiences, increase accuracy, and transform large amounts of data into actionable insights, enabling organisations to make faster, more informed decisions and drive sustainable business growth.

Development timelines vary based on project scope. A proof of concept may take a few weeks, while enterprise AI platforms with multiple integrations can take several months.

Yes. We specialise in legacy system AI augmentation, adding intelligent automation layers, predictive modules, and NLP interfaces to your existing ERP, CRM, or internal tools via clean API architecture, so you don’t have to replace systems that already work.

RAG (Retrieval-Augmented Generation) connects an LLM to your knowledge base, so answers are grounded in your up-to-date data. It’s ideal when information changes frequently. Fine-tuning is better when you need the model to learn your domain’s terminology, style, or specialised tasks. Many projects combine both; we recommend the right approach during architecture design.

Organisations with strict security, privacy, or compliance requirements often need complete control over their AI environments. We deploy AI models within your existing infrastructure, ensuring sensitive data remains protected. Our team supports deployments across AWS, Azure, Google Cloud Platform (GCP), hybrid environments, and fully on-premise systems.

For most businesses, a PoC is the smarter first step. In 4 to 6 weeks, we validate your use case against real data and deliver a working demonstration plus a clear go/no-go decision framework, before you commit to a full production investment.

Businesses are typically ready for AI when they have repeatable processes, access to business data, and clear goals such as automation, cost reduction, customer support enhancement, or predictive analytics. Our discovery phase helps assess AI readiness and identify the highest-value use cases.

Every business operates differently, which is why custom AI solutions often deliver more value than generic tools. Tailored to your specific needs, they can improve efficiency, support future growth, and create a lasting competitive edge in a rapidly changing market.

Generative AI creates content, Machine learning identifies patterns and predictions from data, and AI agents can autonomously perform tasks, make decisions, and interact with systems to achieve objectives.