Production Track Record
- 50+ AI agents deployed to production
- 15+ years combined AI engineering leadership
- 100+ enterprise projects completed
THE TISA · AI Software Development Company
AI Software Development ToAutomateEnterpriseWorkflows
Design, build, and deploy intelligent systems that solve real business problems. From strategy to production-grade AI agents, we handle the complexity so you get measurable outcomes. THE TISA builds production-grade AI agents and generative AI systems for enterprises—not demos. We integrate with your tools, orchestrate LLMs, RAG, and multi-agent workflows, and ship systems that deliver measurable ROI.
Top-rated on major review platforms
Who We
Build For
Our clients run operations teams, support desks and product organisations where downtime costs money. That shapes how we work. We plan releases around your business calendar, not ours. We document handover so your engineers can maintain what we ship. We keep the model layer swappable so a vendor price change or a deprecation notice does not become a rebuild. Some clients bring us in as the whole engineering team. Others place our engineers inside an existing one. The logos below represent companies that have taken a system we built into production. Where a client asked us not to publish their name, we left them off rather than blurring a logo and hoping nobody looks closely.
Security & Compliance
- Secure data handling — your data stays yours
- Encrypted communications and private VPC options
- GDPR-ready, compliance-ready architecture
- No training on customer data
Proven Technology
- OpenAI, Claude, Azure OpenAI, AWS Bedrock
- Production-grade deployments
- Continuous monitoring and optimization
Trusted by leading businesses
What THE TISA
Builds
THE TISA works across two connected pillars and most engagements need both. Our AI team builds agents, retrieval systems and automation that plug into software you already run. Our product engineering team builds and maintains the platforms underneath. We scope them together so the AI layer and the application layer share one architecture instead of two that drift apart in year two. That matters more than it sounds. The most common reason an AI feature gets shelved is not model quality. It is that the surrounding application cannot support the data access, the audit trail or the permission model the feature needs. Use the tabs below to see the full service list under each pillar. Each one links to a page covering scope, stack, delivery timeline and the questions we ask before quoting.
Enterprise AI
AI Agent Development
Build autonomous systems that automate complex enterprise workflows.
Design and ship production AI agents with tool use, guardrails, and measurable outcomes.
AI Agents
Explore →01
Enterprise AI
Generative AI Development
Deploy custom LLM applications, copilots, and RAG systems.
Build generative workflows grounded in your business data—from strategy to production.
Generative AI
Explore →02
Enterprise AI
AI Integration Services
Seamlessly connect AI with your existing tools and systems.
Integrate models, automation, and analytics without rebuilding your entire stack.
AI Integration
Explore →03
Enterprise AI
AI SaaS Development
Launch and scale AI-powered SaaS products for enterprise buyers.
From MVP to multi-tenant platforms with billing, auth, and growth-ready infrastructure.
AI SaaS
Explore →04
Enterprise AI
AI Workflow Automation
Automate high-complexity operations traditional RPA can't solve.
Orchestrate decisions, tools, approvals, and handoffs across production workflows.
Automation
Explore →05
Enterprise AI
AI Strategy Consulting
Clarify use cases, architecture, and ROI before you build.
A focused strategy session to map production AI opportunities to measurable outcomes.
Consulting
Explore →06
Why Teams
Bring Us In
Companies come to THE TISA at one of three moments. They have run an AI pilot that impressed everyone in the room and then went nowhere. They have a product that works but cannot take another feature without a rewrite. Or they need AI capability and cannot hire the team fast enough to build it. We are structured for all three. Our engineers work in your codebase, your sprint cadence and your review process rather than delivering a black box six weeks later. We write the architecture down before we build so you can challenge it. We benchmark models against your data instead of defaulting to whichever vendor we signed with. And we hand over documentation good enough that you could replace us. Most clients do not, but they could.
How We Take a System From
Discovery to Production
Every engagement at THE TISA starts with the workflow, not the model. We sit with the people doing the work and map what they actually do, including the exceptions that never made it into the process document. Those exceptions are usually where the project succeeds or fails. From there we choose the models, the retrieval approach and the integration pattern, and we write the architecture down before anyone writes code. You get to disagree with it while changing it is still cheap. We build, then evaluate against a scored dataset assembled from your real cases, so you see accuracy, latency and cost per task before go-live rather than after. Deployment happens behind approval gates. After launch we monitor output quality and spend, and we tune both as volume grows.
01
Business Problem
Workflow gap
Business goal
Success metric
02
Discovery
Process mapping
User needs
System scope
03
AI Strategy
Use case planning
ROI path
Guardrails
04
LLM Selection
Model fit
Speed & cost
Privacy needs
05
Data Layer
APIs
Documents
Vector search
06
Model Training
Prompt logic
Evaluation
Fine tuning
07
Software Development
Product UI
Backend APIs
Dashboards
08
Testing
Quality checks
Edge cases
Security
09
Deployment
Cloud release
Monitoring
Team handoff
10
Continuous Improvement
Usage data
Prompt tuning
New workflows
Match Your Problem to
a Capability
Most buyers arrive knowing the symptom, not the solution. Support volume is climbing faster than headcount. Analysts spend three days assembling a report that should take an hour. Contract review is the bottleneck in every deal. Nobody can find anything in the shared drive. The matrix below maps those situations to the capability that resolves them and to the delivery approach behind it. Read across the row to see what the system does, what it needs access to and roughly what it takes to build. Some problems need an agent. Some need retrieval over documents and nothing more. A few need neither, and we will tell you when a scheduled job and a clean database would solve it for a tenth of the cost.
AI Capability
We Build
Business Benefit
What It Enables
AI Agents
AI system capability
We Build
Business Benefit
Reduce manual work
Autonomous workflows, tool calling, approvals, and task execution.
Computer Vision
AI system capability
We Build
Business Benefit
Improve inspection
Image analysis, document extraction, visual checks, and detection.
Natural Language Processing
AI system capability
We Build
Business Benefit
Strengthen customer support
Intent detection, classification, summaries, and language intelligence.
Predictive Analytics
AI system capability
We Build
Business Benefit
Improve forecasting
Demand signals, risk indicators, trends, and decision support.
AI Chatbots
AI system capability
We Build
Business Benefit
Deliver 24/7 support
Customer conversations, lead qualification, routing, and escalation.
RAG Systems
AI system capability
We Build
Business Benefit
Activate company knowledge
Secure answers from documents, policies, databases, and internal sources.
AI Automation
AI system capability
We Build
Business Benefit
Create faster operations
Automate repetitive steps across systems, teams, and business workflows.
Recommendation Engines
AI system capability
We Build
Business Benefit
Drive sales growth
Personalized products, content, offers, and next-best actions.
What an AI Agent
Actually Does
An AI agent is software that takes a goal, breaks it into steps and completes them using your tools. That is the whole idea. It reads context from your systems, decides the next action, calls the tool that performs it and checks the result before moving on. When something unexpected happens it re-plans instead of failing. Traditional automation cannot do that. A rule-based script follows a fixed path and stops the moment reality deviates from it, which is why so much RPA needs constant maintenance. The difference matters commercially, not just technically. Rule-based systems handle the 80% of cases you anticipated. Agents handle the remaining 20% that currently lands on a person's desk, and that 20% is usually where the cost sits.
INPUT
User QueryPrompt
AUTH
AI GatewaySecurity
ROUTE
IntentClassifier
Agent CoreOrchestrator
RECALL
MemoryContext
KNOWLEDGE
RAGVector DB
ACTIONS
ToolsAPIs
MODEL
LLMReasoning
SAFE
GuardrailsValidate
RESULT
OutputBusiness
What Ships
at the End
Services describe how we work. This is what lands in production. A support agent that reads the ticket, pulls account history and drafts a reply for review. An internal copilot that answers policy questions with a citation to the source document. A pricing engine that reconciles three systems overnight and flags what does not match. A customer-facing assistant inside your product with usage metering and per-tenant cost limits. A data pipeline that turns unstructured contracts into queryable records. Each one is a working system with monitoring, a rollback path and documentation. None of them are demos. If we cannot describe what a system will do in a sentence like the ones above, it is not scoped tightly enough to build.
Swipe
Inside a Production
AI System
A demo needs a model and a prompt. A production system needs eight more layers, and most of the engineering effort goes into those. Retrieval has to return the right passage from a corpus that grows weekly. The orchestration layer has to handle a tool call that times out. Permissions have to resolve per requesting user, not per application. Evaluation has to run against a scored dataset that gets updated when the business changes. Observability has to tell you which model version produced a bad answer three weeks ago. Guardrails have to catch an output before it reaches a customer. Cost controls have to stop one runaway loop from spending a month of budget overnight. Explore the layers below to see how our engineers assemble them and which decisions we make differently depending on your data and latency requirements.
System 01
The AI Your Business Needs to Win
You don't have time to manage reports. You need insights. Our AI systems think like your best employee - except they work 24/7, never get tired, and learn from every decision.Automate workflows.Predict outcomes.Scale impact.
Live ArchitectureActive Flow
System 02
Agentic AI Development
Deploy AI that handles your tasks automatically without you micromanaging every step.Your AI makes smart decisions on its own.It learns and improves from every task it completes.Your business runs smarter and more efficiently.Your team focuses on strategy instead of repetitive work that slows them down.
Live ArchitectureActive Flow
System 03
AI Automation Consulting
Your business has repetitive work that costs too much time and too much money. We analyze exactly where you are losing efficiency and money.Then we design a custom automation strategy that works for your business.We architect a plan to automate your workflows using AI technology.After implementation, our clients see forty- five percent improvement in efficiency.
Live ArchitectureActive Flow
System 04
Automate Your Entire Business System
Your business has workflows that require constant human attention. Sales, marketing, operations, and HR teams all need their systems to work together smoothly.Right now they operate separately and create manual handoffs.We connect all these systems into one intelligent flow.Our AI engine automatically passes work between departments without manual intervention.
Live ArchitectureActive Flow
System 05
AI Lead Generation Systems
Finding good sales leads is hard. Your sales team wastes time on leads that will never buy. We solve this with AI lead generation. Our system automatically finds people interested in your product. Then it scores each lead to show who is most likely to buy. Next, it qualifies leads to make sure they are real prospects. Finally, it routes the best leads directly to your sales team.
Live ArchitectureActive Flow
System 06
AI Business Process Automation
Your business runs on old manual processes that cause errors and slow everything down. People do things by hand that should be automated. This costs you money in mistakes and wasted time. We transform these manual processes using AI automation. Our AI robots handle repetitive work automatically. They work twenty-four hours a day without making mistakes. They process work faster than humans ever could. The result is powerful.
Live ArchitectureActive Flow
System 07
AI Customer Support Agents
Your customers contact you through email, chat, and phone. Your support team handles each inquiry manually.This is slow and costly.When your team is busy,customers wait.When they are offline, customers cannot get help.We solve this with AI customer support agents.Our AI understands your business and your customers perfectly.It answers questions instantly across all channels.It handles email, chat, and phone automatically.Our AI learns from your knowledge base and improves continuously.The result is powerful.
Live ArchitectureActive Flow
System 08
AI Integration Services
Your business runs on multiple systems that do not talk to each other. Your CRM stores customer data.Your ERP tracks operations.Your legacy systems hold historical data.None of these systems communicate.This creates a broken data picture.Decisions get made on incomplete information.We solve this with AI integration services.We connect all your systems seamlessly.Your CRM, ERP,legacy databases, and new systems all talk to each other automatically.
Live ArchitectureActive Flow
Where We
Go Deepest
We build across sectors but we go deepest where workflows are document-heavy and the compliance requirements are real. Those environments punish generic AI. A support agent in healthcare needs different permission handling than one in ecommerce. A document system in financial services needs an audit trail that survives an examiner asking what happened on a specific date eleven months ago. We start from those constraints rather than adapting a template afterwards. Below are the areas where our team holds the most delivery history, with the systems we have built in each. If your sector is not listed, that does not mean we cannot help. It means we will be honest about the learning curve.
Industry 01
ISVs & Digital Natives
SaaS AI Features, Platform Automation, Product Intelligence
System direction
We build AI features, product copilots, workflow automation, usage intelligence, and scalable platform modules for software businesses.
Launch smarter SaaS products without slowing your core roadmap.
What We
Build On
We do not commit to one model vendor and we advise clients not to either. Accuracy, latency and price move every quarter, and the right choice changes per use case. A classification task that runs ten thousand times a day has different economics than a reasoning task that runs forty times. Our engineers benchmark options against your data before selecting, and we design the model layer so it can be swapped without touching the application around it. That decision has saved clients real money when a provider raised prices or deprecated an endpoint on ninety days notice. The stack below is what we work in most often, grouped by what each layer does rather than displayed as a wall of logos.
Active Technology Layer
AI Models
Foundation models for reasoning, generation, multimodal experiences, and intelligent product features.
OpenAI GPT
Ready to deploy
Claude
Ready to deploy
Gemini
Ready to deploy
ChatGPT
Ready to deploy
Perplexity AI
Ready to deploy
Generative AI
Ready to deploy
Rule-Based Automation vs
AI-Native Systems
Most companies already own automation. RPA scripts, workflow builders, scheduled jobs. Those systems work and we rarely recommend replacing them wholesale. They handle known conditions reliably and they cost less to run. The problem shows up at the edges. When a process changes, a rule-based system needs reprogramming, and the team it was supposed to free up ends up maintaining it. When an input arrives in an unexpected format, it fails silently or escalates to a person. AI-native systems reason about the situation instead of matching it against a fixed path. The comparison below sets out where each approach wins, what each costs to operate and where a hybrid makes more sense than either. We use it in scoping calls because it usually narrows the project before anyone talks budget.
Relative Performance Index
Decision Speed
Traditional
Slow
Digital
Faster
AI-Native
Instant
AI development cost
Estimate What Your
Build Will Cost
Nobody publishes AI pricing because nobody can. A virtual assistant answering routine questions and an agent orchestrating workflows across ERP, CRM and a payment system are not the same project, and quoting one range for both helps nobody. The calculator below asks the questions we ask in a scoping call. How many processes does the system need to understand. How many systems does it connect to. What data sensitivity applies. Where does it need to run. It returns a range with the assumptions written out, so you can see which answer moved the number. Treat it as a planning tool rather than a quote. The purpose is to help you go into a budget conversation with a defensible figure instead of a guess.
Input Layer
Business workflow data
Planning input total
0 hrs
Indicative build band
Add inputs
Cost drivers
Ranges update from your assumptions
Planning hours in scope
0 hrs
Based on your inputs — not a guaranteed saving
Indicative cost band
—
Rough build range from assumed complexity
Primary cost drivers
Process scope
What typically moves the estimate
Assumption check
Add inputs
Fill all fields before using the range
Secure
Your data stays protected.
Instant Results
Savings update in real time.
Trusted Delivery
Built for business systems.
Expert Support
Guidance from planning to launch.
Selected
Work
THE TISA publishes the work we have permission to describe. Each case study covers four things. The problem the client brought us, in their framing rather than ours. The system our engineers built, described concretely enough that you can judge the difficulty. The stack behind it. And what changed after deployment. Where a client approved a number we publish the number. Where they did not, we describe the delivered scope instead, because scope is verifiable and an invented percentage is not. You will notice we publish fewer case studies than some firms our size. That is deliberate. Every project listed is one we can walk you through on a call, including the parts that went wrong.
AI Support Platform01 / 03
Autonomous Support Desk
Customer Operations
Business Problem
Support tickets were routed manually and answers depended on scattered knowledge.
System Built
AI triage, knowledge retrieval, escalation logic, and live performance dashboards.
Outcome
faster first response
62%
AI ticket routingKnowledge assistantTeam visibility
Stack: OpenAI, RAG, Node.js, Help Desk APIs, Analytics
Product Preview
Autonomous Support Desk
What Clients Say About Working
With THE TISA
Founders, product owners and operations leads describe the work in their own words. Each quote carries a name, a role and a company, because an anonymous testimonial proves nothing and everyone reading knows it. We have not edited these for marketing polish. Several mention things that took longer than planned, which we left in. Clients who have worked with agencies before tend to find that more useful than another paragraph of praise. If you want more than a quote, we will arrange a reference call. You can ask them directly how we handled the difficult part of the project, because every project has one and that is the part worth asking about.
Proven Track Record
Results backed by delivery.
0+
Successful Projects Delivered
0+
Countries Served
0+
Industries Built For
0%
Client Satisfaction
5/5
42 reviews
5/5
58 reviews
5/5
26 reviews
Client Story
Client Reviews
Proudly Showcasing Our
Awards & Recognitions
We list recognition where an independent body assessed the work and published the result. Each entry names the issuing organisation, the category and the year, and links to the public record so you can verify it without taking our word for anything. We have left off directory placements that came with an invoice attached. Those are advertising and labelling them as awards would be dishonest. If a badge appears on this page it means an external party evaluated something we built or how we delivered it. Where a listing reflects client reviews rather than a judged award, we say so rather than blurring the distinction.
Verified Platforms
Awards & Recognition
Clutch
Top AI Development Company
GoodFirms
Best Software Development Partner
DesignRush
Verified Technology Company
TechBehemoths
Trusted IT Services Provider
TopDevelopers
Top Developers Recognition
SelectedFirms
Enterprise Delivery Excellence
Before investing in AI,
every business owner has questions.
Explore clear, practical answers about custom AI software, automation, CRM and ERP integrations, project costs, implementation timelines, and expected business outcomes.
Off-the-shelf AI tools work well for common tasks and quick deployment. Custom AI software gives your business greater flexibility and long-term value. THE TISA builds AI solutions around your workflows and integrates them with your existing systems. As an experienced AI software development company we create software that grows with your business instead of limiting it.
Bring Us
the Workflow
Tell us what your team does manually today and we will tell you whether a system can take it over. No pitch deck and no discovery process that bills before it delivers anything. You get one call with an engineer from THE TISA who will map the process, flag the parts that will be difficult and give you a realistic scope. Sometimes that call ends with us saying the problem does not need AI, which happens more than you would expect and saves everyone a quarter. If it does need building, you leave with an approach, a rough range and a clear view of what we would need from your team. Send the form below or email us directly. Either reaches the same people.
Capability Stats
Delivery by capability
Proven volume across agents, automation, integrations, and product engineering.
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AI Agents
Production agent systems
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Workflow Automation
Operational flow builds
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AI Integrations
Systems & API connections
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SaaS Products
AI-native product launches
0+
Generative AI
LLM experiences shipped
0+
Mobile Apps
Mobile products delivered
0+
Web Platforms
Web systems engineered
0+
Cloud Delivery
Cloud deployments managed
0%
Client Retention
Satisfaction score
0+
AI Agents
Production agent systems
0+
Workflow Automation
Operational flow builds
0+
AI Integrations
Systems & API connections
0+
SaaS Products
AI-native product launches
0+
Generative AI
LLM experiences shipped
0+
Mobile Apps
Mobile products delivered
0+
Web Platforms
Web systems engineered
0+
Cloud Delivery
Cloud deployments managed
0%
Client Retention
Satisfaction score