Use Cases for FinBox
How AI is reshaping
the modern enterprise.
Support and Build on one shared memory - track capacity across every engineer, TPM and PM, and turn support signals into proactive fixes. Consolidating your stack, not adding to it.
One shared memory for Support & Build
FinBox × DevRev
The problem, and why now
A business built on delivery -
where planning is the hard part.
130+
lender clients to onboard & run
~50M
credit decisions / month · Sentinel
“1 week”
promise to launch a lending program
$40M
Series B · scaling into SE Asia
3+
tools to piece together to plan capacity
What your lender clients experience
Integrate
APIs
APIs
Go live
Scale
lending
lending
What your teams run to make it happen
Client
implementation
implementation
Support
& escalations
& escalations
Sprints
& delivery
& delivery
Engineer, TPM
& PM capacity
& PM capacity
130+
client rollouts
the same work, repeated
the same work, repeated
The gap
Integrations move data between these tools - not context. So nothing can reason across delivery, support and capacity at once, and planning means stitching tools together by hand. There’s no layer tying them together.
Your current landscape · three surfaces
Good tools on every surface.
One memory would make them greater than their sum.
Engagement
Where your teams already talk
Slack
MS Teams
Microsoft 365
Zoho MailConversations scattered across channels
CX
The requests your clients raise
Freshdesk
HubSpotClient & ticket context, in two places
Engineering
The projects your teams deliver
GitHub
Jira
Confluence
Sentry
Factors.aiDelivery & capacity locked inside the tools
◇ The ask
You asked to consolidate, not add another layer - and to get support and delivery to cross-reference. DevRev unifies all three on one shared memory - one permission model, one audit trail.
The hard truth
Most enterprise AI never leaves the pilot
Not because the models are weak - because enterprise knowledge is fragmented, ungoverned and without shared memory.
95%
of enterprise generative-AI pilots deliver no measurable P&L impact.
Source: MIT / Fortune, 2025
40%+
of agentic-AI projects are projected to be scrapped by 2027 - cost, unclear value, weak controls.
Source: Gartner, 2025
Fragmented knowledge
Pilots don’t scale because enterprise data lives in silos.
Siloed data
Each system holds a slice; no one holds the whole picture.
No business context
Generic models don’t know your batches, SOPs or accounts.
No governance
Compliance & trust become blockers, not enablers.
No shared memory
Nothing compounds; every query starts from zero.
The fix
The winners solve the substrate, not the model - unified, governed, permission-aware shared memory across every system.
Who we are
The enterprise AI platform - built by the team behind Nutanix.
Dheeraj Pandey
CEO & Co-founder
ex-CEO Nutanix - biggest tech IPO of 2016 · Adobe Board
Manoj Agarwal
President & Co-founder
ex-SVP Engineering, Nutanix
$250M+
raised · $1.15B valuation
800+
employees · 8 global offices
1,000+
enterprise customers
Forbes
2024 Best Startup Employers
SOC 2ISO 27001GDPRHIPAAIndia DC
Trusted by
Banking & Financial Services
Technology, SaaS & Retail
Delivery Partners
What customers achieved with Computer
$5M
projected annual savings · BILL (US fintech)
6 hrs
16K tickets migrated across 4 systems · Uniphore
85%
automated resolution · 50% lower cost to serve
3 agents
org-wide (legal, CCO, AI intake) · S&P Global
What we've built
Meet Computer - an AI teammate across every system
Not another chatbot. A teammate that remembers, reasons over your live data, and acts - with your permissions.
Remembers
Persistent memory of your people, systems and past work - sharper over time.
Reasons
Answers over your connected data in plain language - securely, in context.
Acts
Creates tickets, updates records, drafts responses, runs workflows - closes the loop.
Why DevRev
Five reasons Computer wins.
Not another tool bolted on - one shared memory under Support and Build, so your teams get answers, actions and capacity from one place.
Up to 95% fewer tokens
Pre-assembled context, not bloated retrieval. At 50M-decisions-a-month scale - the difference between viable and unaffordable.
Enterprise Benchmark Tool
Same query, same model, head-to-head. Quantify token savings, latency, and accuracy - on your own data.
Prove it
Don’t trust claims
Context, not retrieval
Permission-aware memory assembled before reasoning. Org structure, roles, assets - scoped to what each user can see.
Pre-built
Not per-query
AI that acts, safely
Read AND write back to systems of record. Human-in-the-loop gates for sensitive operations. One-click rollback.
R+W
Not read-only
Every action auditable
Full lifecycle traces on every agent decision. Versioned. SOC 2 Type II, ISO 27001:2022, in-country DC. Compliance from day one.
100%
Traced
One Computer · every surface
One platform for product, engineering, and CX.
Replace fragmented toolchains with a unified AI-native workspace that connects customer signals to every sprint.
SUPPORT
AI auto-resolution
Routine servicing & IT requests resolved end-to-end - no agent touch.
Classify, route & triage
Every ticket understood, prioritised, escalated to a human only when it must be.
Multilingual channels
WhatsApp, telephony, app & web - in your client’s language.
Proactive, alert-correlated
A payment blip becomes a ticket + customer note before the calls come in.
Change-request approvals
Guided approval workflows with the right sign-offs - nothing skipped, everything logged.
Two-way system sync
AirSync keeps Jira, HubSpot & your service desk in step - one guided workflow across all.
One
Platform
BUILD
AI sprint planning
Auto-prioritise backlogs by customer impact; detect dependencies, flag blockers early.
Developer 360
Real-time view of what’s built, its customer impact & sprint ROI, with SPACE metrics.
GitHub & GitLab sync
Issues auto-transition on code activity; every PR, commit & branch links to work items.
Incident management
AI monitors alerts, opens incidents, suggests RCA from past data, broadcasts updates.
Product 360 analytics
CX, product velocity & developer productivity in one view, AI-scored by priority.
CI/CD integration
Jenkins, Harness, CircleCI - issue status auto-updates; release notes generated.
One truth
Support and Build run on one shared memory - a client ticket, the fix that resolves it, and the engineer’s workload all draw on the same source of truth. That’s how capacity planning and cross-referencing finally work.
Support · deep dive 1 of 2 · the agent & customer experience
Every channel, every article - one workspace.
Unified agent workspace
One inbox across email, chat, Slack, WhatsApp & voice - every ticket carries customer history and linked issues.
AI agent assist
Next-best-action, ready-to-send draft replies, similar past tickets and tone help.
PLuG deflection
Conversational bot auto-resolves 50–60% of repetitive issues, then escalates with full context retained.
AI knowledge base
AI-assisted authoring & gap detection - spot missing articles from trending tickets and draft them.
Branded customer portal
Per-brand self-service portal - raise, track & resolve, with KB surfaced before ticket creation.
Co-exist
Start alongside Freshdesk - Computer co-exists via 2-way sync, so you roll out at your own pace with every channel, article and ticket on the same memory as your build queue.
Support · deep dive 2 of 2 · automation, SLAs & governance
Route, resolve and govern - on autopilot.
Intelligent routing
AI sentiment / intent, workload, SLA and schedule-based assignment across units and channels.
Robust SLAs
Per-customer, per-part SLAs; first / next / resolution thresholds with breach & warning.
Custom workflows
No-code rules from microsecond API calls to multi-day approvals - agents act as decision points.
Incident management & session analytics
Cluster Datadog alerts into incidents with AI RCA; see the last 10 sessions before a ticket.
Granular access control
RBAC, group-based roles, customer segmentation and centralised audit - governance built in.
Replace
When you’re ready to fully replace Freshdesk: a large fintech migrated 30M+ objects and 4,000+ workflows onto Computer - delivered in under three months.
Build · deep dive 1 of 2 · capacity & delivery
See capacity, effort and delivery - per person, per sprint.
Developer 360
Capacity, effort distribution & sprint ROI per engineer, TPM and PM - the view you don’t have today.
AI sprint planning
Auto-prioritise backlogs by impact; detect dependencies and flag blockers before they slow teams.
GitHub & GitLab sync
Issues auto-transition on code activity; every PR, commit and branch links to a work item.
Milestones New
Track multi-sprint delivery against dated milestones with rolled-up progress - is the rollout on time?
Product 360 & CI/CD
CX, velocity & productivity in one AI-scored view; Jenkins / Harness / CircleCI auto-update status.
For FinBox
Developer 360 is the direct answer to your capacity question - who’s over-allocated, who has room, where effort is going, across every project at once.
Build · deep dive 2 of 2 · collaboration & what’s new
Deliver by conversation - support signals in every sprint.
Teams New
Group people into Teams with capacity & ownership - Developer 360 rolls up by team.
Chat on enhancements New
Discuss enhancements & features in-thread on the work item - no external chat tool needed.
Incident management
Alert to RCA in minutes - AI summarises the timeline, suggests fixes, broadcasts updates.
Voice-of-customer in sprints
AI clusters support tickets into top product gaps, surfaces revenue impact and drafts the PRDs.
Conversational PM & Jira sync
Plan in plain English on Slack & Teams; write comments & update tickets without opening Jira.
The payoff
Because Support and Build share one memory, a recurring ticket becomes a prioritised, PRD-ready enhancement - the cross-reference you asked for.
What we can solve for FinBox
Your pains - mapped to what DevRev does.
PainNo capacity view across engineers, TPMs & PMs
→
DevRevDeveloper 360 + Teams - effort & free capacity per person and per team, across every project.
PainSupport & delivery don’t cross-reference
→
DevRevOne shared memory links ticket → issue → code; AI clusters tickets into prioritised enhancements.
PainCan’t reuse a build from six months ago
→
DevRevSemantic search & conversational analytics - query past builds; milestones become reusable playbooks.
PainFreshdesk operational drag
→
DevRevAI deflection, intelligent routing, SLAs & session analytics - resolve faster with less manual triage.
PainTool sprawl - “consolidate, don’t add”
→
DevRevReplace Freshdesk + Jira on one platform; HubSpot 2-way sync; runs on Teams, Slack & Outlook.
The platform
DevRev, at a glance
Customer Apps
Chat
Search
Portal
Voice
ComputerDesktop
Mobile
MCP
Internal Apps
Support
Build
Grow
Foundational
Services
Services
Search
Search across your stack.
Workflows
Automate routine work.
Analytics
Act on insights.
Connectors
Marketplace
Marketplace
Bi-directional data syncStructured & unstructured data
Freshdesk
HubSpot
Jira
Confluence
GitHub
Sentry
Wherever your team worksNear real-time sync
SlackMS Teams
OutlookZoho MailMCP
Marketplace
Marketplace
AI agent security
Seven independent enforcement layers.
LAYER 7: DATA PERMISSIONS
Object + Field-level RBAC · Users, Groups & Roles inherited from source systems · Enforced at query time
LAYER 6: HUMAN IN THE LOOP
Approval gates · Confidence thresholds · Action review before execution
LAYER 5: TOOL & WORKFLOW AUTHORISATIONS
OAuth tokens · API keys · Credential vault · Scoped permissions per tool
LAYER 4: SKILLS & TOOL ASSIGNMENTS
Explicit capability grants · Agent can only use assigned tools · Least privilege
LAYER 3: GUARDRAILS
Jailbreak prevention · Prompt injection · Topic boundaries · Output filtering
LAYER 2: AGENT PROMPT
Persona · Domain constraints · Action boundaries · Tone
LAYER 1: SYSTEM PROMPT (IMMUTABLE)
Anti-extraction · Grounding rules · Citation enforcement · Metadata-leak protection
Cannot be overridden by user input or adversarial prompts
Every request passes through all 7 layers. Unauthorised data never enters the agent’s context window.
Security · detailed breakdown
What each layer enforces.
| Layer | Controls | Implementation | Configurable by | FinBox example |
|---|---|---|---|---|
| 1 · System Prompt | Core behaviour, anti-leak | Hardcoded by platform | DevRev only | Cannot be overridden by user input |
| 2 · Agent Prompt | Persona, domain scope | Natural-language instructions | Your admin team | “Only answer lending & onboarding questions” |
| 3 · Guardrails | Jailbreak, injection, scope | Runtime pattern detection + input sanitisation | Platform + config | Blocks “ignore your instructions” attacks |
| 4 · Skills & Tools | Available capabilities | Explicit assignment per agent | Your admin team | Support agent cannot touch the credit-decision API |
| 5 · Authorisations | Credential security | OAuth2, API keys, encrypted vault | Your IT team | Jira & Freshdesk use scoped service accounts |
| 6 · Human in Loop | Approval requirements | Confidence threshold + action classification | Your ops team | Read = autonomous, Write = approval required |
| 7 · Data Permissions | Visibility scope | Object + field RBAC inherited from source | Source system admins | Lender A never sees Lender B’s data |
Measured performance
Enterprise-Bench: architecture measured at production scale.
700 evaluation points · 14 enterprise tasks · 5 data scales (128 → 32,768 records). Answer-preserving methodology: same correct answers, 250× more data noise. Publicly available for independent verification.
100%
Reliability (pass@5)
Structured retrieval passes every task, every trial, every data scale (1× through 256×).
18×
Architecture vs model effect
Data-access architecture moves accuracy 18× more than upgrading the foundation model.
+3%
Cost scaling (1× → 256×)
Knowledge-graph cost is flat across data volumes. API tool-chains grow +37% per scale step.
Structured retrieval (Knowledge Graph)
✓Cross-system joins: 90–100% accuracy
✓Zero accuracy degradation at scale
✓Joins enforced at data layer (structurally correct)
✓Permissions checked before data enters context
API tool-chains (MCP)
✕Cross-system joins: 0–50% accuracy
✕Progressive failure as data grows
✕LLM must maintain join precision (fires shortcuts)
✕60–80% of tokens spent on context re-discovery
“The data-access architecture is not a feature — it is a deployment determinant.” — Enterprise-Bench Methodology, July 2026. Full methodology publicly available for independent reproduction.
Customer proof points · live in production
The moves you’re weighing - already done at scale.
60% of dev ran outside sprints. Velocity never measured. Planning made blind. Moved off Jira - full SDLC on DevRev, Dev360 dashboards, AI-scored PRDs.
1,600Engineers
100%Migrated
DORALive
1,500+ agents on legacy Freshdesk, manual workflows. Moved off Freshdesk - 30M+ objects migrated, L2/L3 Assist Agent, real-time workforce analytics.
700M+Users
6MTickets/mo
1,500+Agents
450 users across 3 brands, in-house AI on Freshservice underperforming. Multi-brand migration, CX deflection agent, HubSpot sync.
>40%Deflection
450Migrated
3Brands
L2 agents manually checking 4 systems for a missing PNR - 15-20 min each. Conversational AI auto-checks payment gateway, booking & CRM.
75%Faster
3-5 minResolve
2000+Flights/day
“Off Jira, off Freshdesk, HubSpot in sync - the exact moves you’re weighing are already live at fintech scale.
How it comes together
Ingest. Connect. Prioritize.
Bring your CX, engineering and comms tools into one knowledge graph - so every client signal, sprint and person’s capacity draws on the same memory.
Ingest
Freshdesk, HubSpot · Jira, Confluence, GitHub, Sentry · Slack, Teams, Outlook, Zoho - 2-way sync.
Connect
One knowledge graph maps a client signal to the ticket, the issue, the code and the engineer who owns it.
Prioritize & act
AI-scored by impact - the same memory answers CX, drives the sprint and shows who has capacity.
Start here
Point Computer at one governed slice - prove a single surface end-to-end on a FinBox-shaped project & ticket set. The data’s ready; we can start with a short pilot.
×
Thank you.
Support and Build. One shared memory. Consolidated, not added.
The AI that works your way