Enterprise AI outcomes you can validate

Support operations — regulated services

The problem. A support organisation in a regulated environment was absorbing high query volumes through manual handling. Response quality mattered as much as speed: in a regulated setting an answer has to stay inside approved language, so throughput could not be bought at the cost of accuracy.

What was built. An AI-assisted response workflow with guardrails, paired with improvements to the self-service answer path so routine questions resolved without reaching an agent at all.

• Retrieval grounded in approved source content
• Guardrails constraining response scope and language
• Human approval retained on the paths that needed it
• Self-service answer quality measured, not assumed

Measured outcome. 55% faster average resolution time and 45% better self-service answer quality after rollout.

Document-intensive operations

The problem. Operational throughput was capped by manual document handling — reading, classifying and routing work that scaled linearly with headcount and degraded under volume spikes.

What was built. An extraction, classification and retrieval-backed review pipeline, deployed in phases rather than as a single cutover so accuracy could be measured against real workload before the process depended on it.

• Structured extraction from unstructured documents
• Classification and routing into existing queues
• Retrieval-based support for human reviewers
• Phased adoption with accuracy checks at each stage

Measured outcome. 70% first-pass accuracy in document handling during phased deployment.

What that number does not say. Seven in ten documents cleared on the first pass; three still required human review. The figure was recorded during phased deployment, not as a finished-state claim. That distinction matters when you are modelling headcount impact.

Financial services workflow modernization — Bengaluru

The problem. Core operations depended on manual document processing and manual drafting, with the usual consequences: cycle time tied to staffing, and first-pass quality varying by who handled the item.

What was built. A governed LLM and retrieval workflow handling triage and draft generation inside core operations — governed meaning decisions are traceable and the model operates inside defined boundaries, which is the precondition for using this in financial services at all.

• Retrieval-grounded triage against operational records
• Draft generation with human review before release
• Decision traceability and access controls
• Integration with existing core systems, not a parallel tool

Measured outcome. ~60% reduction in manual processing effort, alongside improved first-pass response quality.

Operational metrics, not demo metrics

Engagements are measured against metrics the operation already tracks — processing effort, response quality, cycle-time impact — rather than model benchmarks that do not survive contact with production.

Measured before full rollout

Numbers are established during pilot and phased deployment, against real workload, so the decision to scale is made on evidence rather than on a projection.

Acceptance checks defined up front

Each engagement starts with a thin pilot and a written definition of done that maps to measurement. If the acceptance check fails, that is a finding, not something to be reframed.

Confidentiality is the constraint

These engagements sit inside client operations covered by confidentiality, which rules out names, logos and screenshots. Publishing an unverifiable logo wall would not tell you more than this page does.

You talk to the person who built it

Srishti GenAI is led by Ashok Bugude — MIT Manipal and IIT Bombay, 13+ years of engineering leadership, with enterprise delivery experience across EY, Honeywell, Sutherland, Codvo and Zensar. You work directly with the solution architect, not through an account manager.

Ask the hard questions

Bring the ones that expose whether an engagement was real: where the accuracy ceiling sat, what had to stay under human approval, which integration was hardest, what was tried and abandoned. Those answers are difficult to fabricate.