Agents, RAG, and AI products built around your workflows — designed to run in production, not as a demo.
Start with the business problem, then choose the architecture. THE TISA connects strategy, product engineering, models, retrieval, agents, integrations and production operations into one delivery system.
THE TISA designs production AI agents around the work your teams already run—permissions, APIs, business rules and human approval—not isolated chatbot demos. Agents plan, call approved tools, keep state across steps and escalate when a decision needs a person. We ship orchestration, evaluation, logging and fallbacks so the agent can operate in a live environment, not only in a prototype.
We build complete AI applications that combine product UX, data, orchestration, models, integrations and cloud engineering into one production system. That includes identity, role-based access, audit trails, latency budgets and the interfaces operators actually use. THE TISA delivers internal tools, customer-facing products and copilots as software you can run, measure and iterate—not a model wrapped in a thin UI.
THE TISA builds generative AI features that sit inside real workflows: copilots, conversational systems, document generation, extraction and structured outputs. Every experience is designed with evaluation, grounding, format constraints and cost controls from the first iteration. Model choice, prompt design and product engineering stay coupled so output quality can be measured against the job, not against a demo prompt.
We connect models to documents, databases and internal knowledge with retrieval pipelines, hybrid search, reranking, permissions and citations. THE TISA designs chunking, indexing, access control and freshness so answers stay grounded in approved sources as the corpus grows. Evaluation covers retrieval hit-rate, grounding and refusal behavior—so the system can say “I don’t know” instead of inventing a passage.
THE TISA takes an AI SaaS idea from focused MVP to a multi-tenant product with authentication, billing, usage metering, model-cost visibility and scalable infrastructure. Tenancy, rate limits, fallback models and operational dashboards are treated as product requirements, not afterthoughts. You get a launchable application with the controls needed to grow usage without losing track of quality or spend.
We automate classification, routing, document handling and follow-up across the systems you already run—without replacing the process overnight. High-impact steps keep human approval; repeatable reasoning and hand-offs move to the agent or workflow layer. THE TISA maps the current path, defines exception handling and wires orchestration so work completes with an audit trail your operations team can trust.
THE TISA adds models, retrieval, agents and automation to existing applications, CRMs, ERPs, databases, APIs and internal platforms. Integrations respect identity, permissions and the data contracts those systems already enforce. The result is AI capability inside software your teams already open—copilots, extraction, recommendations or agents—without rebuilding the stack from scratch.
The right AI architecture depends on where work slows down, where knowledge is trapped, which decisions repeat, and which systems need to take action.
Production AI agents need more than a prompt. They need identity, context, approved tools, business rules, action limits, memory decisions, observability and clear human escalation.
THE TISA turns foundation models into governed product features with context, structured outputs, workflows, retrieval, validation and application-level controls.
Give teams an AI layer for drafting, research, knowledge access and guided decisions.
Generate data that applications can validate and use instead of relying on uncontrolled free-form text.
Summarize, classify, extract, compare and generate documents inside defined workflows.
Build assistants with context, tools, retrieval, identity and escalation rather than a standalone chat box.
Combine text, image, audio or document inputs when the product needs more than a text-only interface.
Accelerate repeatable generation workflows while preserving templates, review and brand or policy constraints.
Retrieval-Augmented Generation gives an AI system controlled access to the information it needs at request time. The retrieval layer matters as much as the model.
Docs · CRM · DB · Wiki
Parse · clean · enrich
Vector · keyword · hybrid
Select strongest context
Ground answer in evidence
Citations · rules · evals
When AI is central to the user experience, the application layer, tenancy model, cost controls, identity, billing, analytics and operations all matter.
Build customer-facing or internal AI applications where UX, data, model behavior, business logic, permissions and integrations work as one product.
Launch AI-native SaaS products with product economics in mind: multi-tenancy, authentication, subscription billing, usage metering and model-cost visibility.
AI creates more value when it can securely read from and act inside the tools people already use. THE TISA designs the integration and orchestration layer around those existing systems.
Coordinate classification, decisions, actions and approvals across business systems.
Introduce AI capabilities through APIs and integration layers instead of replacing everything at once.
Extract, validate, route and transform information from operational documents.
Give users an AI interface over the tools and data they already work with.
THE TISA can work across commercial and open-source model ecosystems. The objective is to choose the best operating model for the workload—not create unnecessary vendor lock-in.
Choose models against the actual task instead of defaulting to one vendor.
Control instructions, context windows, templates, roles and system behavior.
Return validated schemas that downstream applications can use safely.
Connect LLM reasoning to approved application capabilities and APIs.
Route workloads based on quality, latency, cost, privacy or modality.
Define what happens when a model, retrieval step or external tool fails.
Moving AI into live operations requires more than improving the prompt. The surrounding software has to handle identity, data, integration, evaluation, failures, cost, monitoring and change.
Define the workflow and outcome.
Prove the risky AI assumption.
Measure quality and failure modes.
Build the product and integrations.
Ship with security and observability.
Monitor, learn and improve.
Production AI becomes maintainable when responsibilities are separated: experience, orchestration, knowledge, models, enterprise integrations, and trust controls.
Users · workflows · interfaces · approvals
Agents · prompts · routing · business logic
RAG · search · embeddings · state
LLMs · APIs · MCP · functions · services
CRM · ERP · databases · SaaS · internal APIs
Identity · permissions · evaluation · observability · security
We design model-agnostic architectures where practical, then select the retrieval, orchestration, cloud and observability tools that match the system's quality, cost, security and operational requirements.
OpenAI
Anthropic Claude
Gemini
Llama
Mistral
LangGraphLangChainLlamaIndexCrewAI
MCP
PineconeQdrantWeaviate
pgvector
Elasticsearch
Azure OpenAI
AWS Bedrock
Vertex AIAzure AISageMakerLangfusePromptfoo
Custom evals
TracingCost telemetry
PythonFastAPI
Node.js
Next.js
PostgreSQLAI quality is probabilistic. Production systems need explicit evaluation criteria, representative test cases, traces and regression checks so teams can detect when behavior changes.
Did the answer use the approved evidence?
Did the agent actually complete the intended job?
Did the response match the schema the application expects?
Did search return the most useful context?
Is the experience fast enough for the workflow?
Is model and retrieval spend sustainable at production volume?
Did the system respect permissions and action boundaries?
Did a model, prompt or retrieval change break previous behavior?
AI introduces new data paths, tool permissions and failure modes. We design controls around what the model can see, retrieve, generate and execute.
Use cases should connect to a measurable workflow: faster resolution, less manual review, better knowledge access, improved product experience or more consistent execution.
Industry context changes the data, approvals, integrations, risk profile and acceptable failure modes. We adapt the AI architecture to the environment it will operate in.
Document intelligence, analyst copilots, workflow automation and controlled knowledge retrieval.
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Administrative automation, knowledge access and AI-assisted workflows designed around sensitive-data constraints.
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Search, recommendations, support, catalog intelligence and merchandising automation.
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Operations copilots, document handling, shipment workflows, exception management and planning assistance.
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Lead qualification, document workflows, knowledge assistants and property-data experiences.
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Knowledge retrieval, maintenance assistance, quality workflows and operations automation.
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Learning assistants, assessment support, content workflows and personalized practice experiences.
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Citizen service automation, case workflows and public knowledge assistants.
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Explore selected THE TISA projects where AI is part of the product, workflow or decision layer.
A modern education and career development platform designed to connect academic learning with industry requirements through practical training, live projects, expert mentorship, portfolio building, interview preparation, and placement support.
EZSKU is a B2B product catalog and ordering platform built for beverage and liquor businesses. It helps retailers, restaurants, bars, distributors, and other business buyers explore products and place orders from one platform.
Seed2Sell is an enterprise B2B bulk purchasing and e-commerce platform designed for wholesalers, distributors, manufacturers, suppliers, retailers, and business buyers. It provides an online marketplace where businesses can discover products, compare suppliers, manage bulk purchase needs, negotiate prices, place orders, and track activities in one place.It also provides separate dashboards for each user role to manage their work.
Campus Flow is an AI-powered student portal created to help students manage their academic activities through one online system.
OpsPilot is an AI-powered enterprise administration system developed to manage the academic, operational, employee, and financial activities of a large educational institute.
PeopleCore is an AI-powered employee and academic operations portal developed to manage employee activities and mentor-led student operations within one portal.
NovaMind AI is a multilingual AI assistant that supports voice and text conversations in English, Hindi, and Hinglish.
Ratiwal Estate is a real estate website built for a property business in Jaipur. It helps users learn about the company’s residential, commercial, and investment services and contact the team for guidance. The website is designed for home buyers, sellers, investors, families, and commercial clients. Clear service information and enquiry options make it easier for users to understand the available support before visiting the office. It also gives the business a strong online presence and provides space for adding property listings, location pages, and more services in the future.
Group Hotel Index is an enterprise-scale hospitality directory platform developed to connect group travel planners directly with hotels across the United States.
Speakify is an AI-powered English learning platform for students, professionals, corporate learners, and exam aspirants. The platform supports English and Arabic. Learners can join live classes, watch recorded lessons, practise language skills, take mock exams, and track their progress. AI checks speaking and writing performance during practice. It highlights weak areas and suggests suitable exercises. Separate portals are available for students, instructors, administrators, and parents or sponsors. This helps manage learning, admissions, communication, and payments with less manual work.
Narsik Logistics is a B2B transport and logistics platform developed for a Jaipur-based freight company. It allows customers to explore logistics services, request freight quotations, book transportation, track shipments, and view consignment details.
RentACar is an online car rental and fleet management platform created to simplify vehicle booking and daily rental operations. Customers can search for available vehicles by location, rental dates, price and vehicle type. They can view vehicle details, upload documents, make payments and track their booking status. The Admin Dashboard helps the rental team manage vehicles, bookings, customers, locations, pricing and availability from one place. The platform supports both single-location and multi-branch rental businesses.
Clove is a project management and team collaboration platform for software teams, startups, agencies and growing businesses. It helps teams plan projects, assign tasks, manage sprints and track deadlines in one place. Members can comment on tasks, share files and receive real-time updates.
LockYantra is a passwordless credential and secret management platform for individuals, teams and organisations. It allows users to store passwords, API keys, SSH keys, recovery codes, private notes and confidential documents in one place. Users can sign in through a secure magic link instead of using a traditional account password. The platform allows sensitive information to be shared with team members or clients. Access can be temporary or permanent and can be removed whenever required. LockYantra provides a more secure and organised way to handle important credentials and confidential information.
Narsik Logistics ERP is an internal ERP created to manage the daily work of a Part Truck Load logistics company.
Debit Credit Desk (DCD) is an AI-powered financial platform for individuals, accountants and businesses. Users can record income and expenses, manage ledgers, create budgets and view financial reports. They can also ask the AI assistant questions about spending, savings and cash flow.
SendIt is a smart logistics and transportation platform that connects customers, drivers, fleet owners, businesses and administrators in one system.
Vibe is an AI-powered social media platform that helps users build a digital identity through their activity, relationships and community participation.
Food Delivery On Demand is a mobile food-ordering and delivery platform created for customers, chefs, restaurants and delivery partners.
XYZ News is a mobile news application that brings news videos, reports and daily updates into one place. The app organises content into categories such as political, regional, international, entertainment and viral news, making it easier for users to find relevant updates.
The hardest work often begins after the first successful model response. That is where application engineering, systems integration, evaluation, security and operations determine whether AI creates durable value.
We design the software, integration, evaluation and operational layers required around AI models.
We choose models and providers based on workload constraints instead of forcing every project into one vendor.
Retrieval, orchestration, tools, permissions and human approval are treated as first-class architecture.
AI is connected to the existing systems where work and data already live.
We define what good looks like, create test cases and measure quality instead of relying on demos.
Latency, token usage, model mix, retrieval cost and infrastructure are considered before scale makes them expensive.
Data handling, access, tools and deployment boundaries are planned with the architecture.
Move from opportunity and PoC through product engineering, deployment, monitoring and iteration.
Our process separates business validation, AI validation and production engineering so teams can learn early without pretending a proof of concept is a finished system.
Clear answers on services, cost, timelines, model choice, RAG, agents, data protection, and client-cloud deployment — the questions teams ask before they engage.
THE TISA provides AI agent development, AI application development, generative AI development, RAG systems, AI SaaS development, AI integration, intelligent automation, LLM engineering, evaluation, deployment and optimization. The architecture is chosen around the business workflow rather than forcing every project into the same AI stack.
Cost depends on workflow complexity, data readiness, model usage, integrations, security requirements, interface scope, evaluation needs and deployment model. A focused proof of concept can be substantially smaller than a production system that must connect to multiple enterprise platforms. THE TISA scopes these variables before quoting.
A focused prototype can often be validated faster than a full production rollout. Production timelines depend on data access, integration complexity, evaluation requirements, security review and product scope. THE TISA separates proof-of-concept work from production engineering so the riskiest assumptions can be tested early.
There is no universal best model. Selection should consider quality on the target task, context needs, latency, cost, data handling, tool use, structured-output reliability, deployment options and vendor constraints. Model routing or a model-agnostic architecture may be appropriate when one model cannot satisfy every workload.
Yes. AI can be added to existing SaaS products, internal applications, CRMs, ERPs, databases, APIs and operational systems. The integration approach depends on how the current software exposes data, actions, identity and permissions.
Yes. A RAG architecture can ingest approved knowledge, create searchable representations, retrieve relevant context, apply authorization rules, ground model answers and return citations or sources where the product requires them.
Yes. Agents can use approved tools and APIs to perform tasks such as retrieving records, creating tickets, updating systems, generating documents, coordinating workflow steps or escalating work. High-impact actions can require explicit human approval.
AI security can include data minimization, provider-specific data settings, private or client-controlled deployments, access control, secrets management, RAG permissions, prompt-injection defenses, tool authorization, logging and human review. Requirements are defined for each project.
Yes. THE TISA can work with commercial and open-source model ecosystems. Model choice is made according to the workload, deployment requirements, data handling, quality, latency and operating cost rather than brand preference.
Depending on the use case, we use retrieval grounding, constrained prompts, structured outputs, validation, deterministic business rules, citations, tool verification, evaluation datasets, fallbacks and human review. The objective is to measure reliability rather than assume it.
Yes. Client-controlled cloud deployment can be supported on suitable AWS, Azure or GCP architectures. Some solutions can also combine managed model APIs with client-owned application and data infrastructure.
RAG retrieves relevant external knowledge at request time so the model can answer from current approved context. Fine-tuning adjusts model behavior through additional training examples. RAG is typically used for dynamic private knowledge, while fine-tuning is useful when behavior, format or task specialization needs to change. They can also be combined.
Tell us the workflow, product or business problem you want to improve. We will help you identify the right AI architecture, model, data approach and path to production.