How long does it take to build an AI-based product?

Most AI products take between 4 and 24 weeks. The range depends on how ready your data is, how much model training and testing is needed, and what deployment looks like. Adding AI to an existing system can take extra time for optimization and validation against live data.

When should a business invest in custom AI development?

When off-the-shelf tools cannot deliver the automation, predictions or workflows you need, or when your data and processes are what set you apart. We start with a short consulting and strategy phase so each build is tied to a clear business goal and a way to measure return.

What are the benefits of RAG in enterprise AI projects?

Retrieval-Augmented Generation (RAG) connects a large language model to your own documents and data sources, so answers are grounded in company knowledge rather than the model's general training. It improves accuracy and keeps sensitive data under your control, which makes it a strong fit for chatbots, internal knowledge assistants and generative AI tools.

How do I choose the right AI development company?

Look at real delivered projects, domain knowledge in your industry, security practices and a clear delivery process. Equitysoft has delivered 680+ projects for 310+ clients since 2018, is ISO 27001 certified and GDPR-compliant, and you keep ownership of the code, data and models.

What support do you provide after AI deployment?

We monitor model performance, retrain when data drifts, tune for speed and cost, and apply security updates. That keeps the solution accurate, compliant and aligned with how your business changes over time.

How do you keep AI projects secure?

Security is applied at every phase: secure data handling, access controls, model governance and compliance-driven development (including GDPR and HIPAA where relevant). Our ISO 27001 certified processes cover the full development lifecycle.

How much data do we need to start an AI project?

Less than many teams expect. Generative AI and RAG projects can start with your existing documents and knowledge base. Predictive models need enough historical records that include the outcome you want to predict; we check volume and quality in discovery and tell you plainly if the data is not ready yet, along with what to collect first.

Should we use an LLM API or train our own model?

Most projects start with a hosted model such as GPT, Gemini or Claude, combined with your data through RAG, because it is the fastest route to value. Training or fine-tuning a model makes sense when you need lower running costs at high volume, offline or on-premise deployment, strict data residency or behavior a general model cannot learn from prompts. We compare both on your use case before recommending one.

What does AI development cost?

Cost depends on the use case, data readiness, integration work and how the solution will be hosted. We give a fixed estimate after a short discovery, and many clients start with a proof of concept so the larger investment is only made once the idea has been tested on real data. Running costs such as model API usage and cloud hosting are estimated separately so there are no surprises.

Can you start with a proof of concept?

Yes, and for new use cases we recommend it. A proof of concept uses a sample of your real data to test accuracy and value against the success metric agreed in discovery. If it meets the bar, the same code becomes the base for the production build; if it does not, you have spent a small budget to learn that early.

AI Development Services

Custom AI models, agents, chatbots and integrations, built around your data and deployed into the systems you already run.

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§ Who it's for

Where custom AI development pays off

We build AI that takes work off your team and puts better answers in front of decision makers: workflow automation, predictive models, generative AI, agents and chatbots. Every solution is designed around a business goal, trained on your data and integrated with your apps, CRM or ERP. You own the code, data and models, with no lock-in.

Custom AI is worth building when the problem is specific to your business, the data to solve it already exists, and a generic tool cannot reach either. These are the situations we see most often.

Teams buried in repetitive work
Operations, finance and support teams who re-key data between systems, read documents by hand or answer the same customer questions every day. AI takes the routine part and leaves people the exceptions.
Decisions made on instinct
Leaders sitting on years of sales, usage or operational data with no model turning it into forecasts, risk scores or next-best actions their teams can trust.
Products that need an AI feature
Startups and product teams adding chat, smart search, recommendations, image recognition or content generation to an app their users already depend on.
Off-the-shelf AI that stops short
Businesses whose SaaS AI add-ons cannot see their own data, follow their business rules or meet their security and data residency requirements.
Pilots stuck before production
Companies with a promising notebook or demo that still needs data pipelines, testing, monitoring and integration before it can run every day.

What you get

Repetitive work automated so teams spend time on higher-value tasks
Forecasts and predictions that support faster, better-informed decisions
AI that plugs into your existing apps, CRM and ERP without a platform switch
Security and compliance (GDPR, HIPAA) built in from the first sprint
Models monitored and retrained so accuracy holds as your business changes
Full ownership of code, data and models, with no vendor lock-in

§ What we deliver

What we deliver

Choose an area to see the services in it.

Generative AI

Text, image and media generation tuned to your brand, content and knowledge.

Custom Generative AI Applications
Content and Marketing Automation
LLM Integration (GPT, Gemini and Open Models)
Retrieval-Augmented Generation (RAG) on Company Data
Image Generation for eCommerce and Marketing

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AI Agents

Agents that plan, act and hand off routine work across your tools.

Custom AI Agent Development
AI Voice Agents
Workflow and Process Automation Agents
Multi-Agent Systems

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AI Chatbots and Assistants

Round-the-clock support, lead capture and personalized conversations.

Customer Support Chatbots
Lead Generation Chatbots
Conversational AI and Virtual Assistants
Omnichannel Deployment (Web, App, Messaging, Voice)

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AI Integration

AI added to the apps, CRMs, ERPs and workflows you already use.

AI Integration with CRM, ERP and Business Apps
AI App Development (iOS and Android)
AI Web Development
AI Product Engineering

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Machine Learning and Applied AI

Models that predict, classify, read and see.

Predictive Analytics and Forecasting
Natural Language Processing and Sentiment Analysis
Computer Vision
Robotic Process Automation
Edge AI for IoT and Mobile
Explainable AI (XAI)
Energy-Efficient (Green) AI

AI Consulting

Find the use cases worth building and a roadmap to deliver them.

AI Strategy and Roadmap
Use Case Discovery and Prioritization
AI Readiness Assessment
AI Governance and Responsible AI

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End-to-end solutions

§ Proof

Work that shipped

Featured case study

Smart Skincare with Lisa

Lisa is an AI-powered skincare chatbot that helps users identify skin concerns like acne, dryness, and pigmentation.

Read the case study

Makeup brushes, an eyeshadow palette and cosmetic tools, the crowded choice users face

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How we work

Discovery and use cases
Workshops with your team and a review of your systems and data to find where AI will pay off first.
You receive Prioritized use cases and an AI roadmap
Data preparation
We collect, clean and structure data from internal and external sources so models have a reliable foundation.
You receive Clean, documented training data
Model development
We build and compare supervised, unsupervised, deep learning or LLM-based approaches against the business goal.
You receive Working prototype and evaluation results
Training and tuning
Models are trained on real business data and tuned for accuracy and robustness with cross-validation and transfer learning.
You receive Production-ready model
Deployment and integration
We ship through APIs or event systems on AWS SageMaker, Azure ML or GCP Vertex AI and connect it to your apps.
You receive Live AI feature in your systems
Monitoring and improvement
MLflow, Prometheus and Grafana track performance, and models are retrained as data and needs change.
You receive Monitoring dashboards and retraining plan

§ Tech stack

Technologies we use

Python
TensorFlow
PyTorch
scikit-learn
XGBoost
Keras
OpenAI GPT
Hugging Face
spaCy
Stable Diffusion
OpenCV
YOLO
Pandas
Apache Airflow
Apache Spark
Apache Kafka
MLflow
Kubeflow
Weights & Biases
SHAP
Pinecone
Weaviate
Milvus
FAISS
AWS SageMaker
Azure Machine Learning
Google Vertex AI
Power BI

§ Engagement

Ways to work with our AI team

Discovery sprint
A short, fixed-scope engagement to assess data, shortlist use cases and estimate effort. You leave with a prioritized roadmap whether or not you build with us.
Proof of concept
A working prototype on a sample of your real data, built to answer one question: is this use case accurate and valuable enough to take to production?
Fixed-scope build
Defined deliverables, milestones and price for a use case that has been through discovery, from data preparation to deployment.
Dedicated AI team
Data scientists, ML engineers and developers who work as an extension of your team on a monthly basis, for products with an ongoing AI roadmap.

§ FAQs

Questions, answered

§ In detail

More about our AI Development

DeliverablesWhat you receive from an AI build

Use-case brief and success metric
A one-page agreement on the problem, the current baseline, the metric that defines success and the data sources involved, signed off before development starts.
Prepared, documented data
Cleaned and labeled datasets with a data dictionary and repeatable pipelines, so the model can be retrained without starting from scratch.
Model or LLM application with an evaluation report
The working model, prompt chain or RAG pipeline, plus test results on held-out data and a plain-language note on where it performs well and where it does not.
APIs and integrations
Secured endpoints connected to your app, CRM, ERP or data warehouse, with authentication, rate limits and error handling in place.
Monitoring and retraining plan
Dashboards for accuracy, latency and running cost, drift alerts, and a schedule for when and how the model is retrained.
Source code and handover
Repositories, model artifacts, infrastructure scripts and runbooks transferred to you. You own everything we build.

Responsible AISecurity, privacy and oversight built in

Data protection
Encryption in transit and at rest, role-based access, and only the data the use case needs. Personal data is masked or kept out of prompts and training sets where possible, in line with GDPR.
Human in the loop
Low-confidence predictions and high-impact actions go to a person for review. Thresholds are agreed with your team and adjusted as trust builds.
Explainability
For scoring and prediction models we show which inputs drove each result (for example with SHAP), so analysts and auditors can follow the reasoning.
Cost and performance control
We choose the smallest model that meets the target, cache repeat requests and track cost per call, so running costs stay predictable as usage grows.

§ Contact

Plan your AI project with our engineers

Book a free consultation. We will review your use case and data and suggest the most practical first step.

Email: business@equitysoft.in