Our Process

AI Development is
Different From
Software Development

>

Most vendors use a waterfall process: requirements → design → build → test → deploy. This works for traditional software. It doesn't work for AI.

AI development is more iterative, more uncertain, and more dependent on experimentation. You don't know the full requirements until you understand the data. You don't know if the approach will work until you test it. You iterate based on results.

Our process is designed for this reality.

See 4 Phases
AI development process simulation

Outcome loop

Explore → Validate → Ship

Our Methodology

Outcome-focused. Iterative by design.

The difference is profound. We're not trying to build exactly what was requested. We're trying to achieve the outcome you need.

Instead of

Build X, test X, ship X

We do

Define outcome → explore → prototype → validate → iterate → ship

The loop

Define outcome
explore
prototype
validate
iterate
ship

We're not trying to build exactly what was requested. We're trying to achieve the outcome you need.

The Process

4 Phases. One clear path.

From go/no-go honesty to continuous optimization — designed for AI reality, not waterfall theatre.

01

Phase 01 · 2–4 weeks

Discovery & Strategy

This is where we figure out if AI is even the right solution.

What We Do

  • Deep conversation about your business challenge
  • Understand your current process and pain points
  • Define success metrics (what does “winning” look like?)
  • Assess data availability and quality
  • Evaluate technical feasibility
  • Consider implementation timeline and cost

How Long: 2–4 weeks

Your Involvement: High. We'll need your time to understand the problem deeply.

Discovery and strategy orbit diagram

Why This Phase Matters

Most AI projects fail at this stage because the problem is wrong. We spend time getting it right before writing code.

“Why is this a problem?”

Understand root cause

“How much would solving this be worth?”

Quantify impact

“What data do we have?”

Understand constraints

“Who needs to change for this to work?”

Organizational readiness

“What's the timeline?”

Realistic expectations

Output

Clear problem statementSuccess metrics definedData assessmentProposed approachRough timeline and investmentGo/no-go recommendation

Honestly

If we think AI won't help, we'll tell you. We'll recommend alternative approaches. We'd rather have this conversation now than blow 6 months and your budget.

02

Phase 02 · 3–6 weeks

Data & Architecture

Now we design the system before building it.

What We Do

  • Audit your existing data
  • Design data pipelines (how data flows through the system)
  • Select the right technical approach (which model architecture, framework, etc.)
  • Design the system architecture (how components connect)
  • Define evaluation strategy (how we'll measure model performance)
  • Identify risks and mitigation strategies
  • Create detailed technical specification

How Long: 3–6 weeks

Your Involvement: Medium. We'll present findings and get your input on approach.

Data and architecture pipeline

Why This Phase Matters

Architecture is often invisible, but bad architecture creates problems: systems that don't scale, models that fail in production, security vulnerabilities, unreliable deployments. We design for reliability, security, and scalability from the start.

Data audit

“Your data is 60% usable after cleaning, missing fields in 30% of records”

Architecture decision

“We'll use LangChain + GPT-4 + RAG because it handles your use case well”

Pipeline design

“Data flows from your database → cleaning → enrichment → model → API”

Risk assessment

“If data quality drops below 50%, model performance will degrade. Here's how we monitor for it”

Output

Technical architecture diagramData flow specificationModel selection and justificationEvaluation strategyRisk assessment and mitigationImplementation timeline with milestonesCost estimate
03

Phase 03 · 8–16 weeks

Build & Iterate

We build in increments. You see working software regularly.

How Long: 8–16 weeks

Your Involvement: High. We need feedback every 2 weeks.

Build and iterate sprint loops

Sprint 1: Core model + basic API

  • After 2 weeks, you have working AI you can test
  • We measure performance on real data
  • We iterate based on results

Sprint 2: Integration with your systems

  • The AI system now talks to your databases
  • Real data flows through the system
  • We measure real-world performance

Sprint 3: Optimization and refinement

  • We improve model accuracy
  • We optimize for speed and cost
  • We add features based on feedback

Sprint 4+: Advanced features, monitoring, deployment prep

  • Continuous improvement of model
  • Set up monitoring and alerting
  • Prepare for production launch

Why This Approach

You see progress every 2 weeks (not waiting 6 months)
We catch problems early (not discovering them at launch)
You can give feedback and we adjust (not locked into original requirements)
We validate assumptions with real data (not guessing)

What Happens in Practice

Week 1–2“Here's a working model that gets 87% accuracy on your test data”
Week 3–4“We integrated with your CRM. Here's model performance on real data: 78% accuracy (lower because data is messier). Here's why.”
Week 5–6“We retrained on more data. Accuracy is now 82%. We added monitoring. We found an edge case (handles X properly now).”
Week 7–8“Performance is stable. Let's do a pilot with 5% of your traffic.”

Output

Working AI systemPerformance metricsDeployment packageDocumentationTraining for your team
04

Phase 04 · Ongoing

Deployment & Optimization

We don't hand over code and disappear.

How Long: Ongoing

Your Involvement: Medium ongoing. Weekly check-ins, monthly strategy calls.

Deployment monitoring dashboard

Week 1–4 After Launch

  • Monitor system closely
  • Catch any production issues immediately
  • Make real-time adjustments
  • Gather user feedback

Ongoing (Months 2–12+)

  • Monitor performance: Track accuracy, speed, cost, errors
  • Retrain models: When performance drifts, retrain automatically
  • Optimize: Find ways to make it faster/cheaper/better
  • Add features: Support new capabilities as your needs evolve
  • Security updates: Keep system current and secure

How It Works

Automated monitoring: We watch your system 24/7. Alerts if something goes wrong.
Weekly reviews: We discuss performance, issues, improvements
Monthly optimizations: We retrain models, improve performance
Quarterly strategy: We discuss what's working, what's not, where to go next

How We're Different

Traditional vs The TISA

Traditional Software Development

Requirements → Design → Build → Test → Deploy → Done

Problems

  • • Requirements change during build
  • • Hidden issues found during test (expensive to fix)
  • • Deploy, then discover problems in production
  • • Support team deals with problems

Our AI Development Process

Discovery → Architecture → Build+Iterate → Deploy+Optimize → Continuous Improvement

Advantages

  • We iterate based on real data
  • Problems surface early (easy to fix)
  • We validate in production carefully
  • We stay involved and fix problems
  • System gets better over time

Key Principles

Principles of our process

01

Outcome-Focused

We don't build what was requested. We build what solves your problem. If your goal is “handle 70% of support queries,” we measure that. If we achieve 62%, we iterate. If we achieve 78%, we optimize cost. Success is defined by outcomes, not features.

02

Data-Driven Decisions

We use real data to make decisions. Instead of: “We think this approach will work.” We do: “Let's test this approach on real data and see.” Data tells us what's working and what isn't.

03

Early Validation

We validate assumptions early, not late. We prototype in Week 3, not Month 6. We test on real data in Week 4, not Month 8. We discover problems when they're cheap to fix.

04

Iterative Improvement

We don't expect perfection on launch. We ship 80% quality, gather feedback, improve to 90%, gather more feedback, improve to 95%. Better to have a good system getting better than to wait for perfect.

05

Transparency

You can see everything. Code, metrics, decisions, problems. No hidden work. No surprises when we say something is done.

06

Risk Management

We identify risks early and mitigate them. “This data quality is a risk. Here's how we'll handle it.” “This approach might not scale to 1M users. Here's how we'll verify.” “This technology is new. Here's our backup plan if it doesn't work out.”

Timeline Expectations

How long does it take?

e.g., AI-powered personalization engine

5–6 months to working system

  • 01Discovery
    3 weeks
  • 02Architecture
    4 weeks
  • 03Build
    12 weeks
  • 04Deploy & Optimize
    Ongoing

These timelines assume:

  • You're available for input when needed
  • Your data is accessible
  • You're making decisions promptly
  • You're committing to iterations

If any of these aren't true, timelines extend.

Cost Model

No hourly surprises.

We use a project-based pricing model (not hourly, not T&M).

How It Works

  • We define scope, timeline, deliverables
  • We quote a fixed price
  • You pay 50% upfront, 50% at completion
  • Scope changes are discussed and priced separately

Why Fixed Price

  • You know the cost upfront (no surprises)
  • We're incentivized to deliver efficiently
  • We own our estimates
  • Clear accountability

What's Included

  • All team members for project duration
  • Ongoing support for 3 months post-launch
  • Performance optimization
  • Documentation and training
  • Monitoring setup

What's NOT Included

  • Long-term maintenance (after 3 months)
  • Scaling to 10x traffic (that's a new project)
  • Major scope changes (quoted separately)

Small project

$40–60K USD

Medium project

$80–150K USD

Large project

$200–400K USD

Actual pricing depends on complexity, team size, technology stack.

Why We're Affordable

  • We're based in Jaipur (not Silicon Valley rates)
  • We work efficiently (no unnecessary overhead)
  • We don't subcontract (you're working with us directly)
  • We move fast (less time = lower cost)

Quality Assurance

Rigorous checks on every project

Code Review

Every line of code is reviewed by someone other than who wrote it. We catch bugs, security issues, and design problems.

Testing

  • Unit tests: Every function tested
  • Integration tests: Components work together
  • Edge case tests: What happens with bad data?
  • Performance tests: Does it scale? Is it fast enough?
  • Security tests: Can anyone break in? Does data stay private?

Model Validation

  • Performance on test data
  • Performance on real data
  • Performance on edge cases
  • Performance over time (does it degrade?)
  • Fairness and bias checks

Production Validation

  • Monitor errors and anomalies
  • Track performance metrics
  • Verify business outcomes
  • Catch regressions immediately

Communication

Clear and often

We communicate clearly and often.

Daily

  • • Slack or email updates if anything is blocked
  • • Team coordination on what's being worked on

Weekly

  • • Demo call: See working software, give feedback
  • • Status update: What got done, what's next
  • • Problem-solving: Address any issues together

Bi-Weekly

  • • Strategy review: Are we on track? Do we need to adjust?
  • • Technical deep dive: Explain architectural decisions
  • • Roadmap planning: Next steps

Monthly

  • • Business review: How are we progressing toward outcomes?
  • • ROI check: Is this investment paying off?
  • • Planning: What's next?

What Success Looks Like

When our process works well

  • ✓Discovery is thorough — We understand your problem deeply
  • ✓Architecture is sound — No major surprises during build
  • ✓Build is iterative — You see progress every 2 weeks
  • ✓Deployment is smooth — No major production issues
  • ✓Optimization is continuous — System gets better over time
  • ✓Outcomes are clear — You can measure the business impact
  • ✓Communication is open — No surprises, no misunderstandings
  • ✓Partnership is genuine — We're invested in your success

This is what we aim for on every project.

When Things Go Wrong

Sometimes they do. Here's how we handle it.

  1. 01 Problem IdentifiedWe catch it immediately through monitoring
  2. 02 EscalationWe escalate to leadership within 1 hour
  3. 03 CommunicationWe tell you what happened and how we're fixing it
  4. 04 InvestigationWe figure out root cause
  5. 05 FixWe fix the problem
  6. 06 PreventionWe implement safeguards so it doesn't happen again

We don't hide problems. We surface them, fix them, and learn from them.

Post-Project Support

After launch, you have options

Option 01

Ongoing Partnership

~$2–5K/month depending on system complexity

  • Continuous monitoring
  • Quarterly optimization
  • Monthly strategy reviews
  • Priority support

Most clients choose Ongoing Partnership because systems get better over time, model performance improves, new opportunities emerge, and we catch problems before they impact you.

Your Role in the Process

We do the work, but you're essential

01

Discovery Phase

  • Provide access to stakeholders
  • Share data and systems
  • Give feedback on proposed approach
  • Make decisions on scope and timeline
02

Architecture Phase

  • Review and approve technical design
  • Clarify any questions
03

Build Phase

  • Participate in weekly demos
  • Give feedback on working software
  • Make decisions on iterations
  • Test and validate in your environment
04

Deployment Phase

  • Monitor system in your environment
  • Report issues or concerns
  • Give us feedback on performance
  • Help us understand user behavior
05

Optimization Phase

  • Share feedback on what's working/not working
  • Help us prioritize next improvements
  • Participate in strategy reviews

We're a team. Your input matters.

Why This Process Works

Both sides win

For You

  • Clear timeline and cost
  • Visibility into progress
  • Ability to influence direction
  • Measurable outcomes
  • Long-term partnership
  • Risk mitigation

For Us

  • We can actually deliver what we promise
  • We learn from each project
  • We build relationships, not one-off deals
  • We're rewarded for quality, not cutting corners

This process creates a partnership where both sides win.

Ready to Get Started?

The first step is a Discovery conversation.

We'll:

  • Listen to your challenge
  • Ask hard questions
  • Assess feasibility
  • Discuss timeline and investment
  • Explain our process in detail
  • Tell you honestly if we're a good fit

No pressure. Just conversation.

Let's figure out if AI can help your business. And if it can, how The TISA can help you build it right.