Divyansh Verma / Technology Consultant

Complex systems. Useful software.

I help financial-services and technology teams turn complex requirements into software products, connected systems and reliable workflows.

Discuss a project Explore my work

Computer science engineer Architecture through implementation Fintech and wealth management

Illustrative system

Product APIs Operations Data Cloud

Product

Shape the software around the job people need to complete.

APIs

Connect systems with clear contracts and recovery paths.

Data

Make information dependable and usable.

Cloud

Design deployment and operation around real constraints.

Operations

Make failures, ownership and next actions visible.

018+ years in technology

02Fintech and wealth-management experience

03Architecture through implementation

01 Selected work

Problems I work on

My consulting work sits under client confidentiality, so these are anonymised patterns rather than named case studies. Each one is a problem I am regularly brought in to solve.

Read these as representative scenarios. They are drawn from recurring problems in real engagements, with client names, systems and internal detail removed. The diagrams are conceptual illustrations, not screenshots of a client system, and the scenarios are not presented as measured case-study outcomes.
Scenario 01 / Integration architecture

The same record disagrees in three different systems

Account, party and document data moves between a core platform, an operations tool and a reporting layer. Each hop mostly works, so nobody owns the gaps. Teams reconcile by hand and trust in the data quietly erodes.

The problem
Point-to-point syncs built at different times, with no shared contract, no retry semantics and no single place to see what failed.
My contribution
Map the real data flows against the systems of record, agree which system owns each field, and design the integration and error handling with the teams who operate it.
Technical approach
Defined API contracts, idempotent writes keyed on stable identifiers, explicit retry and dead-letter handling, and reconciliation checks that surface drift instead of hiding it.
Current status
Anonymised pattern from consulting work. Shared as an approach, not as a published performance metric.
Conceptual diagram: one hub, clear contracts
Scenario 02 / Fintech workflows

Account opening stalls in the space between teams

A new account needs data entry, document collection, internal review and a handoff to a custodian or downstream platform. The individual steps are understood. What nobody can answer quickly is where a specific application is stuck, and why.

The problem
Workflow state lives in spreadsheets, inboxes and people's memory, so exceptions are invisible until a client asks.
My contribution
Walk the workflow with the operations team, write down the states and transitions that actually occur including the unhappy ones, then design the system around that reality.
Technical approach
An explicit state model with an audit trail, validation moved as early as possible, document and custodian integration behind clear interfaces, and a queue that shows what needs a human decision.
Current status
Anonymised pattern from fintech and wealth-management work. Described as a design approach, not a certified product.
Conceptual diagram: explicit states and exceptions
Scenario 03 / Document systems

Documents exist, but nobody can account for them later

Statements, agreements and correspondence arrive through several channels and land in folder structures that made sense to whoever created them. Two years later, answering a straightforward question about what was sent and when takes days.

The problem
Storage was solved and retrieval was not. Metadata is inconsistent, access rules are implicit and there is no reliable record of who did what.
My contribution
Model the metadata that retrieval and review actually depend on, then design ingestion, permissions and audit trails around those questions rather than around the folder tree.
Technical approach
Normalised document metadata separate from binary storage, ingestion that classifies at the point of arrival, role-based access, append-only activity logging and retention-aware lifecycle rules.
Current status
Anonymised design pattern. It is careful, retention-aware design and I do not present it as a certified records-management product.
Conceptual diagram: storage plus a separate index

More problem patterns I work on

02 Capabilities

Four groups of work

Technology choices follow the problem rather than a fixed stack. Select a group to see what it covers.

Turning business requirements into a system design somebody can actually build. Scoping, architecture, delivery sequencing, and hands-on engineering through implementation and production readiness.

Designing and repairing the connections between systems. API contracts, data exchange, middleware patterns, bulk and batch handling, and the retry, reconciliation and error visibility that integrations need before anyone will trust them. Salesforce and MuleSoft are part of this experience where they are already in the landscape.

Cloud-enabled services, data pipelines and processing workflows, plus the operational automation that removes repetitive manual work. The aim is fewer moving parts that fail quietly, not more infrastructure.

Account workflows, custodian and platform connectivity, documents and financial data, where accuracy and auditability matter as much as the interface.

On applied AI, I help teams implement and evaluate specific use cases such as extraction, classification and summarisation, then measure whether the output is good enough for the task. I say so when ordinary deterministic software is the better answer. I build with existing models and tooling, and I do not develop proprietary models or claim research credentials.

03 How I work

Good software needs more than a working demo.

A demo proves an idea is possible. Getting it into daily use means deciding what happens when inputs are wrong, a dependency is down or the person who built it has moved on.

Step 01

Understand

Business context, users, constraints and the systems already in place. I want to know what happens today, including the workarounds.

Step 02

Design

An architecture and integration approach sized to the team that has to run it, with the trade-offs written down rather than assumed.

Step 03

Build and integrate

Implement the software and connect it to the systems around it, including the awkward paths that only appear with real data.

Step 04

Validate

Test the assumptions and the edge cases, not just the happy path. Agree what has to be true before this goes live.

Step 05

Operate

Make the system supportable: monitoring that means something, clear ownership, and a handover that does not depend on me.

Requirements

I separate the stated request from the underlying job. That usually changes the scope, and it is cheaper to find out early.

Trade-offs

Cost, speed, flexibility and operational load pull against each other. I make the choice explicit and name what it gives up.

Failure recovery

Retries, idempotency, dead-letter handling and reconciliation. A system that cannot recover from a bad hour is not finished.

Testing and readiness

Automated coverage where it earns its keep, plus a readiness check on access, data migration, monitoring and rollback.

04 About

Engineer first, consultant second.

A short introduction to how I work and where my financial-services experience comes from.

Divyansh Verma

DV

Computer science engineer
Technology consultant
Founder

I am Divyansh, a computer science engineer and technology consultant. I work across the software lifecycle, from understanding the business problem and designing the system to building it, integrating it and getting it ready to run.

Much of my work has been connected to financial services and wealth management. In that setting, reliability, data quality, failure recovery and being able to explain what happened matter as much as the visible interface. That shapes how I design things everywhere else.

My approach is technology-agnostic. The problem and its constraints should decide the tools, not the other way around. I have deep enterprise-platform and integration experience, including Salesforce and MuleSoft, and I treat it as useful background rather than my identity. divyansh.tech is my consulting practice: founder-led, and you work with me directly.

I hold a BE in Computer Science Engineering, postgraduate Business Analytics education through BITS Pilani WILP, and fintech education including the Indian School of Business.

FinEcosystem

In development

A financial-technology product venture aimed at recurring operational problems in financial services and wealth management. Product and systems exploration rather than a finished, certified platform.

Sarvahit Innovations

Early stage

A mission-driven technology venture focused on access to opportunities, participation and accountable impact. Early stage, and building in the open.

My founder story, writing and interests are at meetdivyansh.com

05 Contact

Let's work through the problem.

Tell me what you are building, what is getting in the way, and where you need help.

Discuss a project Email me directly

divyansh3007@gmail.com LinkedIn