CTO
Shivam
Shivam leads aibrevo’s engineering team and has personally worked on 450+ CRM and AI automation projects across his career, spanning enterprise CRM implementations and AI-driven automation builds. He leads a 16-person engineering team based in San Francisco, working alongside a distributed group of remote specialists.
What Shivam leads at aibrevo
Shivam runs aibrevo's engineering organization day to day alongside founderAlpit Patel — architecture decisions, platform certifications across the team, and the technical standard behind everyCRM implementation the firm delivers. His own project history spans both sides of the work aibrevo does: CRM data models and integrations, and the AI-driven automation layered on top of them — the same combination behind aibrevo'simplementation guides across Pipedrive, GoHighLevel and monday.com.
450+ CRM and AI automation projects
That count spans Shivam's full career, not aibrevo's engagements alone — it includes work across CRM implementations and AI automation builds for clients beyond aibrevo as well. It's the reason the team's default answer to a platform question tends to be specific rather than theoretical.
It is worth being clear about what the number is and is not. It is a career-long count of CRM and AI automation projects, so it is not aibrevo's own engagement count, which the firm states separately as 350+ implementations since 2021. The two figures answer different questions: one describes an individual's accumulated experience, the other the firm's delivery history. We would rather you weigh them separately than treat them as one bigger number. What the count does support is a working familiarity with how projects tend to go wrong, which is what the rest of this page describes.
What does a CTO of a CRM implementation firm actually do?
The CTO owns the technical quality of everything aibrevo delivers: what gets designed, how it gets built, and how it gets checked. The sections below describe aibrevo's engineering standard, the one Shivam's team works to. It is written as firm methodology, not as a personal résumé; the only career facts on this page are the ones stated at the top.
Architecture decisions
Which objects exist, how they relate, where data of record lives, and how systems exchange it. These decisions are hard to reverse, so they are made deliberately and written down.
The technical standard
What good looks like for a data model, a permission set, an integration and an automation, applied consistently across eight platforms so quality does not depend on which engineer is assigned.
Team structure and assignment
Matching work to the right specialist: Salesforce work to Salesforce engineers, migration work to the data and integration group, GoHighLevel and AI work to the automation specialists.
Platform certification
Keeping engineers current on the platforms they deliver, since vendors change their products constantly and yesterday's best practice can become tomorrow's workaround.
Escalation and second opinions
Being the person a project team can turn to when a design choice is contested, or when an integration behaves in a way the documentation does not explain.
How should a CRM data model be designed?
A CRM data model should be designed backwards from the questions the business needs answered, using standard objects wherever they fit and custom ones only where they do not. It is the part of a project that is cheapest to get right early and costliest to fix late, which is why aibrevo signs off the model before building anything.
Start from the questions the business will ask
A data model is a set of answers waiting for questions. We list the reports, forecasts and handoffs the business needs, then work backwards to the objects and fields that make them possible. Fields that answer no question are candidates for deletion before they are ever created.
Use standard objects until they genuinely break
Every platform ships with a tested model for contacts, companies and deals. Custom objects are justified when the business has an entity the standard model cannot represent, such as a policy, a property, a subscription or a project. They are a poor way to avoid a conversation about process.
Decide the record of truth for every field
When two systems both hold a customer's phone number, one must win. We name the system of record for each shared field and the direction of sync. Ambiguity here produces the slow, maddening bugs that come from two systems overwriting each other.
Design permissions as carefully as fields
Who can see, edit, export and delete is part of the model. Permissions retrofitted after launch are painful because people have already formed habits around what they could see.
Plan for the data you will have, not the data you have now
A model that works for 2,000 records may creak at 200,000. We ask about growth so that the design does not need to be rebuilt in a year.
What makes a CRM integration reliable?
A reliable integration is built on documented APIs, assumes the other system will sometimes fail, avoids creating duplicates when it retries, and leaves a trail that lets someone trace any record. Most integration problems that reach us are missing one of those four properties.
Prefer official APIs and native connectors
Where a vendor provides a documented API, we build against it rather than around it. Unofficial workarounds break when the vendor changes something without notice, and they do.
Assume the other system will fail
Every integration needs an answer to what happens when the target is down, rate-limited or returns something unexpected. That answer might be a retry, a queue, an alert to a named person, or all three.
Make sync idempotent
Running the same update twice should not create two records. Duplicate creation from retried webhooks is one of the most common integration defects, and it is preventable with a stable identifier and an upsert pattern.
Log enough to answer 'what happened to this record?'
When a customer says they never got a follow-up, someone must be able to trace the record through the integration and see where it stopped. Without logging, the answer is a shrug.
Document authentication and ownership
Every credential should belong to a service account, not a person who might leave, and its location and renewal date should be written down. Integrations that die when an employee's account is disabled are an avoidable classic.
The implementation guides go further on integrations, migration and common automation failures for specific platforms.
When should you use configuration and when should you write code?
Use configuration whenever the platform's admin tools can do the job, and write code only when configuring around a limitation would produce something fragile. Code is a liability as well as a capability: someone has to read it later.
Configuration first
If the platform's admin tools can do it, that is the default. Configuration is cheaper, easier for your admin to modify and survives platform upgrades better than custom code.
Code where configuration would be contorted
Apex on Salesforce, Power Automate and custom connectors on Dynamics 365, or a custom API integration are justified when configuring around a limitation would produce something fragile and unreadable. The test is whether a future admin could understand it.
Write custom code as if a stranger will maintain it
Because one will. Naming, comments and a short note on why the code exists matter more than cleverness.
Keep the escape hatch small
The more logic that lives in custom code, the more the business depends on whoever can read it. We keep that surface as small as the requirement allows.
How should AI automation be layered onto a CRM?
AI automation should be layered onto a CRM only after the data model is sound, with clear limits on what the agent can say and change, and with a human path always open. Shivam's stated experience spans both CRM implementation and AI automation builds, and the two depend on each other: an agent is only as good as the records it can read.
Decide what the agent is allowed to do
An AI agent that answers questions, qualifies leads or books appointments needs explicit boundaries: what it may say, what it may change in the CRM and when it must hand off to a person.
Ground it in the CRM's real data
An agent is only as useful as the context it can read. That makes the data model and data quality prerequisites for AI automation rather than separate topics.
Keep a human path open
Every automated conversation needs a clear route to a person, and every handoff needs to carry the context so the customer does not repeat themselves.
Test it against awkward inputs
Real customers write in fragments, change their minds, and ask things nobody anticipated. We test agents against those cases before launch and monitor them after.
Treat it as maintained software
Prompts, knowledge and rules drift out of date. An AI layer with no owner degrades in the same way an unowned CRM does.
What does QA look like before a CRM goes live?
QA at aibrevo starts before building, with design review, and continues through sandbox testing, awkward-case testing, migration reconciliation and user acceptance, ending with handover and a 30-day support window. The stages are deliberately sequential, because each catches a different kind of defect.
- 01
Design review before build
The data model and permissions plan are reviewed and signed off before configuration. Catching a wrong relationship at this stage costs a conversation; catching it after automation exists costs a rebuild.
- 02
Build in a sandbox where the platform provides one
Where a platform offers a sandbox or test environment, changes are exercised there before they touch production data. Where it does not, we build in a way that can be tested safely.
- 03
Test the normal path and the awkward one
Each automation and integration is run with realistic records and deliberately hostile ones: missing fields, duplicates, unusual characters, records with two owners.
- 04
Reconcile migrations
Record counts and sampled records are compared between source and destination. Aggregate totals can look right while specific relationships, such as which contact belongs to which company, are wrong.
- 05
User acceptance with the people who will use it
The client's own team runs the daily workflow before launch. They find the friction that engineers cannot see, because engineers do not do the job.
- 06
Handover and a support window
Documentation, recorded training and 30 days of post-launch support catch what real use reveals and transfer ownership to your admin.
How does the engineering approach differ by platform?
The process is the same on every platform, but the risks differ: Salesforce tends toward over-engineering, HubSpot toward speed outrunning structure, Airtable toward loose data design. Each platform has its own dedicated implementation page.
Salesforce
The most flexible and the easiest to over-engineer. The engineering discipline is restraint: use standard objects, keep Apex for what configuration cannot do, and treat governor limits and deployment as design constraints from day one.
HubSpot
Fast to value, so the risk is speed outrunning structure. We focus on property hygiene, lifecycle stage definitions, and making sure marketing and sales agree on what a qualified lead is before workflows are built on top of it.
Microsoft Dynamics 365
Strongest when treated as part of the Microsoft environment. The engineering emphasis is Power Automate flow design, security roles, and integration with the tools the organisation already runs.
Zoho CRM
The value is the suite, so integration between Zoho apps is the work. We map how data should flow between them so the team does not end up re-keying it.
Pipedrive
Simple by design, which is a virtue. We resist adding complexity that turns a clean pipeline into an enterprise system nobody asked for, and put effort into stage definitions and activity discipline.
monday.com CRM
A work management platform used as a CRM. The engineering question is where boards should be linked and where they should stay separate, so that the flexibility does not become sprawl.
Airtable
A relational database with a friendly face. Base design, linked records and interface design matter most, since a poor structure is easy to build and expensive to unpick.
GoHighLevel
An all-in-one platform where the engineering effort goes into pipelines, workflows, calendars and messaging compliance, and into making sure automations fail visibly rather than silently.
What technical mistakes break CRM builds most often?
The most common technical failures are colliding automations, values hard-coded into logic, platform limits discovered too late, permissions that were never tested by role, and integrations that drift out of sync without anyone noticing. Each is avoidable with a habit applied consistently.
Workflows that loop or collide
Two automations that both update the same field can trigger each other indefinitely, or overwrite one another depending on run order. We map which automations touch which fields before adding another, and prefer fewer, clearer workflows to many overlapping ones.
Hard-coded values inside logic
IDs, email addresses and thresholds buried in a workflow or script are invisible until they are wrong. We keep such values in one visible, documented place so an admin can change them without reading code.
Bulk operations that hit platform limits
Every platform limits API calls, record volumes or execution time. A design that works on ten records can fail on ten thousand. Migrations and bulk updates are planned around those limits rather than discovering them mid-run.
Permissions that leak or block
Too open, and sensitive data is visible to the wrong people. Too tight, and people share logins or export data to spreadsheets to get their work done. Both are design failures, and both are found by testing as each role, not as an administrator.
Silent data drift
Integrations that mostly work slowly desynchronise systems. Periodic reconciliation, comparing counts and samples across systems, catches drift before a customer does.
Dependence on one person
A system only one engineer understands is a risk. Documentation, readable code and a second person who has seen the design are cheap insurance.
What should a CRM handover include from an engineering point of view?
An engineering handover should include the signed-off data model, the permissions plan, a list of every automation and integration with its owner, the location and renewal date of every credential, and a plain-language note on any custom code. If an admin cannot answer "why does this exist?" from the documentation, the handover is incomplete.
It should also include a short list of known limitations and deferred items, so nobody mistakes a deliberate omission for a defect. Honest documentation of what the system does not do is as useful as documentation of what it does, and it prevents the same question being asked and answered repeatedly over the following year.
How can you judge any CRM partner's engineering quality?
You can judge a CRM partner by asking a handful of specific questions about design, failure handling, ownership, verification and independence, and listening for concrete answers. These are the questions we would ask if we were the buyer, and you are welcome to put every one of them to aibrevo.
Ask to see a data model diagram from a previous project, with client details removed. A team that designs first will have one.
Ask what happens when an integration fails. A good answer names retries, alerts and logging. A vague answer means it has not been thought through.
Ask who will own the system after launch and what training is included. If the answer is 'you can call us', the design is a dependency.
Ask how migrated data is verified. Reconciliation of counts and samples is the minimum.
Ask what is deliberately left out of the first release. A team that cannot name it has not prioritised.
Ask whether they would recommend a different platform. An independent partner should be able to say yes.
How is the engineering team structured?
Shivam leads a 16-person engineering team based in San Francisco, working alongside a distributed group of remote specialists. Work is organised by platform and by discipline: platform specialists, data migration and integration engineers, and GoHighLevel and automation specialists.
Delivery is in-house. The firm does not resell another company's delivery team, and the engineer who understands why a pipeline stage exists is the one who configures it. For the commercial and scoping side of the same process, readAlpit Patel's page, and for how a project runs from first call to support window, see about aibrevo.
To put a technical question to the team directly, use the free30-minute callor send a message.
What should you know about aibrevo's engineering team?
Who is Shivam at aibrevo?
Shivam is aibrevo's CTO. He leads the 16-person engineering team based in San Francisco, working alongside a distributed group of remote specialists, and has personally worked on 450+ CRM and AI automation projects across his career, spanning enterprise CRM implementations and AI-driven automation builds.
Are the 450+ projects all aibrevo engagements?
No. The figure covers Shivam's whole career, including CRM implementations and AI automation work for clients beyond aibrevo. aibrevo's own count is 350+ implementations since 2021. The two numbers measure different things and should not be added together or compared directly.
How does aibrevo decide between configuration and custom code?
Configuration is the default because it is cheaper, easier for your admin to change and more resilient to platform upgrades. Custom code such as Apex, Power Automate or API work is used when configuring around a limitation would be fragile. Either way, it gets documented for whoever maintains it.
How do you test integrations before go-live?
Integrations are exercised with realistic and deliberately awkward records, and failure paths are tested as well as success paths: what happens when the other system is down or returns unexpected data. We also check that retries do not create duplicates, and that errors reach a named person.
Can aibrevo build AI agents on top of a CRM?
Yes. AI automation is part of the work, layered on the CRM's data model. We define what the agent may say and change, ground it in real CRM data, keep a human handoff open, and test it against awkward inputs before launch. Prompts and rules need an owner afterwards.
How do you handle a CRM that another team built badly?
We start with an audit of the data model, automations and integrations to see what is sound and what is not, then recommend repair, partial rebuild or full rebuild. Cleaning up a previous implementation is a regular project type, not an edge case.
What happens to the documentation and code after handover?
Everything built is documented, including why decisions were made, and your admin is trained in recorded sessions. Custom code is written to be readable by someone else. The intent is that your team owns the system and does not depend on aibrevo to maintain it.
How do I talk to an engineer before committing?
Book the free 30-minute architecture review. You speak with someone technical about your setup and leave with a written read on it, whether or not you hire aibrevo. If a simpler approach would serve you better, that is what the read will say.
Talk to an engineer
A free 30-minute architecture review — a written read on your setup, whether or not you hire us.