Use Cases for Bizom
How AI is reshaping
the modern enterprise.
AI-first customer support on one shared memory - deflect repetitive tickets, close knowledge gaps, and turn every support signal into a proactive fix. With Build in the same memory, so support and engineering finally see one picture.
One shared memory for Support & Build
Bizom × DevRev
The problem, and why now
750+ brands to support -
where every ticket is a relationship.
750+
CPG brands to support & retain
8M+
retailers on the platform
35+
countries & languages to serve
$12M
Series B · Real Intelligence & AI
3+
tools between support & engineering
What your brand customers experience
Raise a
question
question
Get
resolved
resolved
Stay &
grow
grow
What your teams run to make it happen
Support
& deflection
& deflection
Knowledge
base
base
Escalate
to engineering
to engineering
Fix in
the build queue
the build queue
1000s
tickets a week
the same questions, repeated
the same questions, repeated
The gap
Support answers in one tool, engineering fixes in another - integrations move data between them, not context. So a recurring ticket never becomes a prioritised fix, the KB never closes its gaps, and support & engineering never see one picture. Build is the complement that closes the loop - support is where it starts.
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
Google Meet
Slack
Gmail
Zoho MailConversations scattered across channels
CX
The requests your customers raise
Zoho Desk
Zoho CRMCustomer & ticket context, in two places
Engineering
The projects your teams deliver
Bugzilla
GitHubWWiki
Delivery & 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
Technology & SaaS
Banking & Financial Services
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 Bugzilla, Zoho CRM & Zoho 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.
Multilingual PLuG chat
Answers & deflects in the customer’s own language - auto-detected across your Africa & Indonesia markets, on one KB.
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 Zoho Desk - 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 Zoho Desk: a large enterprise migrated 30M+ objects and 4,000+ workflows onto Computer - delivered in under three months.
Support · deep dive 3 of 3 · analytics & the support↔engineering loop
Every ticket, every trend - in one live dashboard.
Ticket Insights, out of the box
SLA compliance, CSAT, average & median resolution, escalations, unassigned & awaiting-response - no BI project.
Slice & drill
Filter by date, stage, part or customer; click any widget to drill straight into the underlying tickets.
Support ↔ engineering transparency
Tickets Linked to Issues shows how much support load is really product work - and which part & team owns it.
Voice-of-customer into the roadmap
Recurring ticket clusters surface as prioritised enhancements - the loop back into Build closes automatically.
Transparency
The cross-reference you asked for, made measurable: Tickets Linked to Issues shows exactly how much of support is product work - so support and engineering finally see the same picture.
Support · SFA app & backend session analytics
See what the field user did - before they ever raised a ticket.
Replay the Android & iOS SFA app
Watch the exact mobile session that led to the ticket - every screen, tap and scroll from your field salesforce, played back.
App crashes & error capture
App crashes, dead taps and the exact stack & network errors from the session - no more “works on my device”.
Outlet, scheme & PJP context
See the outlet, scheme mismatch or PJP step in play when it broke - the field context an agent can’t get over chat.
Backend reports & dashboards too
The same replay covers the admin & manager web backend - the session rides on the ticket so engineering gets a reproducible bug.
Faster RCA
Every ticket carries the last sessions before it was raised - so support sees exactly what the field salesperson or manager hit (app crash, scheme mismatch at an outlet, a PJP snag), and engineering gets a bug it can reproduce on the first try.
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 Bizom
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 & Bugzilla sync
Plan in plain English on Slack & Google Meet chat; write comments & update bugs without opening Bugzilla.
The payoff
Because Support and Build share one memory, a recurring ticket becomes a prioritised, PRD-ready enhancement - the cross-reference you asked for.
Use case · Computer + Zoho CRM
Ask about any account - get a live 360, not a static record.
Ask in plain language
“What’s the status of the FreshPicks account?” - tier, status and open deals, straight from Zoho CRM.
Cross-system by default
CRM deals + Zoho Desk support health (blocker / high / med) + linked Bugzilla issues - in a single answer.
Risk summary, reasoned & cited
Computer weighs the deal, ticket load and linked bugs to explain slip risk - with confidence and sources.
Act without switching tools
Draft the follow-up, update the CRM field or open an issue - all from the same thread, with your permissions.
For Bizom
Your sales & CS teams live in Zoho CRM - now they can interrogate it conversationally and see the support & engineering picture attached to every account.
What we can solve for Bizom
Your pains - mapped to what DevRev does.
PainToo many repetitive tickets, low self-serve
→
DevRevSelf-serve KB deflection - the CX agent answers from your articles first, deflecting 50–60% before a ticket is ever raised.
PainKnowledge base has gaps & stale articles
→
DevRevAI gap detection & authoring - spots missing articles from trending tickets and drafts them, so the KB keeps closing its own gaps.
PainNo transparency between support & engineering
→
DevRevOne shared memory links ticket → issue → code; Tickets-Linked-to-Issues shows exactly how much support is product work.
PainRecurring issues never reach the roadmap
→
DevRevVoice-of-customer clustering - AI groups tickets into prioritised, PRD-ready enhancements and pushes them straight to Build.
PainTool sprawl - “consolidate, don’t add”
→
DevRevReplace Zoho Desk + Bugzilla on one platform; Zoho CRM 2-way sync; runs on Google Meet, Slack, Gmail & Zoho Mail.
Depth you can’t shortcut
What it takes to build something this deep.
What a platform this deep is made of
Ten systems, stitched into one. This is why it took us four years to build.
Data pipelines & ETL
Vector database
Embedding jobs
Agent orchestration
Auth & RBAC layer
Evals & guardrails
Monitoring dashboards
Connectors: Zoho, Bugzilla…
Prompt & version mgmt
Cost controls
Every box is a product in its own right. Every wire is an integration we engineered, hardened and rebuilt. Four years, 800+ people — so that none of this complexity ever reaches your users.
One platform — DevRev
A knowledge graph that already connects everything.
Graph + RAG built in
Agent Studio, low-code
RBAC & audit, native
Connectors out of the box
All of that depth, delivered as one product — which is how Paytm went live in just 8 weeks.
That’s the difference a knowledge graph makes.
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
Zoho Desk
Zoho CRM
Bugzilla
WikiW
GitHub
Wherever your team worksNear real-time sync
SlackGmailGoogle MeetZoho 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 | Bizom 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 | Bugzilla & Zoho Desk 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
“AI-deflected support, one shared memory, delivery in sync - the support-led transformation you’re after is already live at enterprise 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
Zoho Desk, Zoho CRM · Bugzilla, Wiki, GitHub · Slack, Gmail, Google Meet, Zoho Mail - 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 Bizom-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