Our Process

AI development is different from software development.

Most vendors still waterfall: requirements, design, build, test, deploy. That works for traditional software. It does not work for AI.

You do not know the full requirements until you understand the data. You do not know if the approach will work until you test it. You iterate on results, not on a frozen spec.

Our process is built for that reality — four phases, one measurable outcome.

Explore the phases

Our Methodology

We follow an outcome-focused, iterative process.

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

A fixed sequence

Build X, test X, ship X

Our approach

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

The delivery path

Four phases. One measurable outcome.

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

PHASE 012–4 weeks

Discovery & Strategy

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

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

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

Questions we explore

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

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

What you receive

  • Clear problem statement
  • Success metrics defined
  • Data assessment
  • Proposed approach
  • Rough timeline and investment
  • Go/no-go recommendation

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.

PHASE 023–6 weeks

Data & Architecture

Now we design the system before building it.

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

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

What this looks like

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

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.

What you receive

  • Technical architecture diagram
  • Data flow specification
  • Model selection and justification
  • Evaluation strategy
  • Risk assessment and mitigation
  • Implementation timeline with milestones
  • Cost estimate
PHASE 038–16 weeks

Build & Iterate

We build in increments. You see working software regularly.

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

Build in working increments

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 we work this way

  • 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)

An illustrative progression

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.

What you receive

  • Working AI system
  • Performance metrics
  • Deployment package
  • Documentation
  • Training for your team
PHASE 04Ongoing

Deployment & Optimization

We don't hand over code and disappear.

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

First weeks in production

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

Ongoing optimization

  • 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 support 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 it compares

Built around evidence and feedback

Traditional software can waterfall. AI cannot — we iterate on real data, then stay after launch.

Traditional Software Development

Requirements → Design → Build → Test → Deploy → Done

  • 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

  • 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

Our principles

The decisions behind the process

Outcomes, data, early validation, iteration, transparency, and risk — in that order.

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.”

Planning

Indicative project timelines

Ranges, not promises — they hold when you're available, data is accessible, and decisions move.

Small Project

e.g., AI chatbot for internal use

3–4 months to working system

Discovery
2 weeks
Architecture
2 weeks
Build
8 weeks
Deploy & Optimize
Ongoing

Medium Project

e.g., AI-powered personalization engine

5–6 months to working system

Discovery
3 weeks
Architecture
4 weeks
Build
12 weeks
Deploy & Optimize
Ongoing

Large Project

e.g., Complete business process automation

7–8 months to working system

Discovery
4 weeks
Architecture
6 weeks
Build
16 weeks
Deploy & Optimize
Ongoing

These estimates assume

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

Commercial model

Scope, investment, and what's included

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

How pricing 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

Indicative investment

Small project$40–60K USD
Medium project$80–150K USD
Large project$200–400K USD

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

Included

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

Quoted separately

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

Why fixed scope

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

How we manage cost

  • 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 & visibility

How we validate the work

Code review, tests, model checks, and production monitoring — not a launch-and-hope handoff.

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

Working together

A clear communication rhythm

Daily unblock, weekly demos, bi-weekly strategy, monthly business review.

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?

Shared definition

What a successful engagement looks like

You can measure the outcome. You can see the work. You are not surprised.

Success markers

  • 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

When something goes wrong

A defined path from issue to prevention

Catch it, tell you, fix it, then make sure it does not happen again.

  1. 01

    Problem Identified

    We catch it immediately through monitoring

  2. 02

    Escalation

    We escalate to leadership within 1 hour

  3. 03

    Communication

    We tell you what happened and how we're fixing it

  4. 04

    Investigation

    We figure out root cause

  5. 05

    Fix

    We fix the problem

  6. 06

    Prevention

    We implement safeguards so it doesn't happen again

After launch

Choose the support level you need

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.

Ongoing Partnership

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

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

Support Plan

~$500–1K/month

  • Access to our team for questions
  • Priority response (4 business hours)
  • Monitoring and alerting
  • Bug fixes and updates

Self-Serve

$0/month (but you handle issues)

  • You manage the system
  • We provide documentation
  • Available for consulting as needed

Your role

What we need from your team

Access, decisions, and honest feedback at each phase — we cannot iterate in a vacuum.

Discovery Phase

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

Architecture Phase

  • Review and approve technical design
  • Clarify any questions

Build Phase

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

Deployment Phase

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

Optimization Phase

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

Why it works

A process that supports both teams

This process creates a partnership where 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

Ready to Get Started?

The first step is a Discovery conversation.

No pressure. Just conversation.

  • 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

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