You don't need another AI proof-of-concept. You need software that ships.
We build custom AI tools (agentic workflows, automation engines, intelligent dashboards, content pipelines) in 2-week sprint cycles. Working demos at every milestone. Deployed in 4-16 weeks.
Most custom AI projects die between the demo and production.
The handoff gap
A strategy firm told you what to build. Now you need someone to actually build it. The dev shop you're evaluating has never seen the diagnostic, doesn't understand why this use case was prioritized, and is pricing based on hours. You end up managing two vendors who don't talk to each other, and the thing that ships barely resembles what was recommended.
Proof-of-concept purgatory
Your team or a vendor built a working prototype. It demoed well. Everyone was excited. That was four months ago. The prototype still isn't in production because nobody planned for authentication, data pipelines, error handling, edge cases, monitoring, or the seventeen integration points with your existing systems. A demo is not a product. The gap between the two is where budgets and timelines balloon.
Off-the-shelf tools that almost work
You've evaluated the SaaS options. They handle 70% of what you need, but the remaining 30% is exactly the part that makes your business different. You're paying for features you don't use, can't customize the ones that matter, and your data lives in someone else's infrastructure. The total cost of ownership for a tool that 'almost works' often exceeds the cost of building exactly what you need.
Internal teams that can't context-switch
Your engineering team is capable, but they're maintaining your core product. Pulling two senior engineers off the roadmap to learn AI tooling, experiment with prompt engineering, and build infrastructure they've never built before will cost you 3-4 months of product velocity, and there's no guarantee they'll get it right the first time.
Scoped. Sprinted. Shipped.
Every build engagement follows a structured process designed to keep scope tight and get working software in your hands early.
Scoping & Architecture
Week 1
We define exactly what's being built, how it integrates with your existing systems, and what 'done' looks like, before writing a single line of code. If you've completed our AI Strategy Diagnostic, this phase goes much faster because the business case, data audit, and opportunity scoring are already done. For new clients, we conduct a compressed discovery focused specifically on the build scope. The output is a Technical Architecture Document that covers system design, data flows, API integrations, AI model selection, infrastructure requirements, and a sprint plan with milestones.
Deliverable: Technical Architecture Document, sprint plan with milestones, success metrics framework, and fixed-scope agreement
Sprint Cycles
Weeks 2-13 (varies by scope)
We build in 2-week sprints. Each sprint follows a consistent rhythm: sprint planning (day 1) to align on what's being built, development (days 2-8), internal QA (days 9-10) before you see anything, client demo (day 10) where you interact with working software and give direct feedback, and feedback incorporation (days 11-14) to adjust based on what you've seen. You never go more than two weeks without seeing tangible progress. Every demo is working software, not wireframes or slide decks. If something isn't right, we catch it in week 2 or 4, not month 4 or 6.
Deliverable: Working software increment every 2 weeks, sprint retrospective notes, updated backlog and timeline
UAT & Deployment
Final sprint
The last sprint is dedicated to user acceptance testing, production deployment, and handoff. We run structured UAT sessions with your team, resolve any final issues, deploy to your production environment (or ours, if preferred), configure monitoring and alerting, and conduct hands-on training for the users who'll interact with the system daily. Deployment includes full documentation: technical architecture, API docs, runbooks, and a troubleshooting guide your team can reference without calling us.
Deliverable: Deployed system, complete technical documentation, user training sessions, monitoring and alerting setup
Post-Launch Support
2 weeks after deployment (included)
Every build engagement includes two weeks of post-launch support at no additional cost. We monitor system performance, resolve any issues that surface with real-world usage, optimize model performance based on live data, and make sure your team is confident running the system on their own. We don't hand off until the thing we built is actually working — in your environment, with your data, with your users.
Deliverable: Performance monitoring report, optimization adjustments, handoff confirmation, optional retainer proposal for ongoing support
Scoping & Architecture
Week 1
We define exactly what's being built, how it integrates with your existing systems, and what 'done' looks like, before writing a single line of code. If you've completed our AI Strategy Diagnostic, this phase goes much faster because the business case, data audit, and opportunity scoring are already done. For new clients, we conduct a compressed discovery focused specifically on the build scope. The output is a Technical Architecture Document that covers system design, data flows, API integrations, AI model selection, infrastructure requirements, and a sprint plan with milestones.
Deliverable: Technical Architecture Document, sprint plan with milestones, success metrics framework, and fixed-scope agreement
Sprint Cycles
Weeks 2-13 (varies by scope)
We build in 2-week sprints. Each sprint follows a consistent rhythm: sprint planning (day 1) to align on what's being built, development (days 2-8), internal QA (days 9-10) before you see anything, client demo (day 10) where you interact with working software and give direct feedback, and feedback incorporation (days 11-14) to adjust based on what you've seen. You never go more than two weeks without seeing tangible progress. Every demo is working software, not wireframes or slide decks. If something isn't right, we catch it in week 2 or 4, not month 4 or 6.
Deliverable: Working software increment every 2 weeks, sprint retrospective notes, updated backlog and timeline
UAT & Deployment
Final sprint
The last sprint is dedicated to user acceptance testing, production deployment, and handoff. We run structured UAT sessions with your team, resolve any final issues, deploy to your production environment (or ours, if preferred), configure monitoring and alerting, and conduct hands-on training for the users who'll interact with the system daily. Deployment includes full documentation: technical architecture, API docs, runbooks, and a troubleshooting guide your team can reference without calling us.
Deliverable: Deployed system, complete technical documentation, user training sessions, monitoring and alerting setup
Post-Launch Support
2 weeks after deployment (included)
Every build engagement includes two weeks of post-launch support at no additional cost. We monitor system performance, resolve any issues that surface with real-world usage, optimize model performance based on live data, and make sure your team is confident running the system on their own. We don't hand off until the thing we built is actually working — in your environment, with your data, with your users.
Deliverable: Performance monitoring report, optimization adjustments, handoff confirmation, optional retainer proposal for ongoing support
Production software, not a proof-of-concept.
Architecture (Week 1)
- Technical Architecture Document with system design, data flows, and integration specs
- Sprint plan with defined milestones, deliverables, and success criteria
- Fixed-scope agreement with transparent pricing
Build (Sprint Cycles)
- Working software demo every 2 weeks, interactive, not slide-based
- Sprint retrospective notes and updated project timeline
- Ongoing access to the development team via dedicated Slack/Teams channel
Deployment & Handoff
- AI system deployed to your infrastructure (or managed hosting)
- Complete technical documentation: architecture, API docs, runbooks
- User training sessions (recorded for future onboarding)
- Monitoring and alerting configuration
- 2 weeks of post-launch support included
We build the tool. These come before or after.
Tight scoping keeps timelines honest and costs predictable.
AI strategy, diagnostic, or opportunity assessment
If you haven't identified which AI use case to build yet, start with our diagnostic. It tells you exactly what to build and why, and makes the build engagement faster and more focused.
AI Strategy & DiagnosticGo-to-market strategy, demand generation, or content marketing
If the AI tool you're building is customer-facing or revenue-generating, you'll need a growth engine behind it. We handle that as a separate engagement, often running alongside the later build sprints.
Growth & MarketingNon-AI operational automation and workflow design
Sometimes the diagnostic reveals that the highest-value intervention isn't AI at all, it's process automation, system integration, or workflow redesign. Those are scoped separately.
Operations AutomationIs this the right fit?
Right for you if
- You've identified a specific AI use case with a clear business case, either through our diagnostic or your own analysis, and you're ready to commit budget and executive attention to building it.
- You have an internal engineering team but don't want to pull them off the product roadmap to learn AI tooling from scratch, and you need a team that can deliver working AI software on a fixed timeline.
- You've been burned by a failed AI pilot or a proof-of-concept that never reached production, and you want a build partner with process discipline and a record of shipping.
Not right if
- You haven't yet identified what to build or why. Start with our AI Strategy & Diagnostic service so you don't build the wrong thing expensively.
- You're looking for a quick chatbot wrapper or a simple API integration that your internal team could ship in a week. Our engagements are for systems where business logic, data pipelines, and user experience all matter.
The kinds of systems we build.
Problem
A mid-market NBFC was spending 40+ analyst hours per week on manual document verification across loan applications. Turnaround time was 3 days per application, and error rates were climbing as volume grew.
What we did
Built an agentic document processing pipeline: automated extraction of key data from bank statements, ITRs, and identity documents using vision models and structured parsing, cross-referenced against internal risk criteria, and routed flagged applications to human reviewers with pre-populated assessment summaries. Integrated with the existing LOS via API.
Outcome
85% of verification tasks automated. Average processing time dropped from 3 days to 4 hours. Analyst team redeployed to higher-value underwriting decisions. Estimated annual savings: INR 1.2 Cr.
Problem
A D2C brand scaling to INR 100Cr revenue was producing 200+ pieces of content monthly across product descriptions, social media, email campaigns, and blog posts. The content team was bottlenecked, quality was inconsistent, and brand voice drifted with every new freelancer.
What we did
Built a custom content pipeline powered by fine-tuned language models trained on the brand's existing high-performing content. The system generates first drafts for product descriptions, social captions, and email sequences, all in the brand's voice, with human review as the final step. Included a dashboard for the content lead to manage queue, approve drafts, and track output metrics.
Outcome
Content production capacity doubled without additional headcount. First-draft acceptance rate: 72% (up from 30% with previous freelancer workflow). Time from brief to published: reduced from 5 days to 1.5 days.
Problem
A logistics SaaS company with 150+ enterprise clients was drowning in customer support. First-response time had ballooned to 8 hours, resolution time was over 48 hours, and the support team was spending 60% of their time answering the same 25 questions in slightly different ways.
What we did
Built an AI-powered support triage and response system. Incoming tickets are automatically classified by urgency, category, and complexity. For the top 25 recurring question patterns, the system generates contextual draft responses pulling from the knowledge base, product docs, and the customer's specific configuration. Complex tickets are routed to the right specialist with a pre-populated context summary.
Outcome
First-response time dropped from 8 hours to 23 minutes. Tier-1 ticket volume reduced by 58%. Support team redeployed to customer success and expansion revenue activities.
What a build engagement looks like end to end.
85% reduction in manual verification, 3-day to 4-hour turnaround
A lending platform processing 500+ loan applications per week engaged us after completing our AI Strategy Diagnostic, which identified document verification as the highest-ROI automation opportunity. The scoping sprint took 5 days, faster than usual because the diagnostic had already mapped the workflow, assessed data readiness, and quantified the business case. We built the system in four 2-week sprints: Sprint 1 delivered the core document extraction pipeline covering bank statements and ITRs. Sprint 2 added identity verification and cross-referencing against internal risk criteria. Sprint 3 built the reviewer dashboard and exception handling workflows. Sprint 4 focused on integration with the existing LOS, load testing, and production deployment. UAT ran for one week with the underwriting team processing live applications in parallel with the existing manual workflow. After confirming accuracy and reliability, we cut over fully. Two weeks of post-launch support resolved minor edge cases around unusual document formats. The system now processes 85% of applications without human intervention. The remaining 15% reach a human reviewer with pre-populated assessments, reducing their review time by 60%. Estimated first-year savings: INR 1.2 Cr.