Get Amp'd!

Turn AI pilots into enterprise-wide business value with
Gemini Enterprise

From Gemini Enterprise to production value

Agivant Technologies launches a dedicated Gemini Enterprise practice with Google Cloud, turning ambitious AI pilots into production-grade agents that deliver real business outcomes every day.
Built on the Gemini Enterprise Agent Platform, Google's Agent Development Kit (ADK), and production-grade architectures, we help organizations advance from early prototypes to resilient, self-orchestrating agentic environments in days and weeks.
Ajay Malgaonkar

The shift to the agentic enterprise is redefining how businesses operate, innovate, and grow. Our dedicated Gemini Enterprise practice helps organizations move beyond experimentation and into real, enterprise-wide impact.

Ajay Malgaonkar
Global Chief Delivery & Solutions Officer, Agivant Technologies
See the accelerators in action

Get Amp’d to move from AI ambition to autonomous workflows

Enterprise AI proves its worth in production, not in the pilot. Amp’d is how Agivant delivers real business value for every enterprise, bringing together Agivant’s AI Engineering toolkit: Bolt, forward-deployed engineers, production-grade AI agents, and reusable engineering assets. Together, they move AI from ambition to autonomous workflows. Every build amplifies the next.
Get Amp'd to move from AI ambition to autonomous workflows

Put coordinated agent teams to work across core enterprise systems

On Gemini Enterprise, Agivant builds multi-agent teams that connect directly with existing CRM, ITSM, and ERP systems. One agent orchestrates the workflow while specialist agents parse, research, build, and validate. Work moves continuously, and speed is the first change an enterprise feels. The proof is measurable:
Quoting cycles compress from days to minutes
Sourcing cycles fall by 50%
Engineering teams reach 2x productivity as the queue runs itself
CPQ migrations move at 2x speed with lower risk
Speed matters most when the enterprise stays in control.
Keep teams in control while agents run the work

Keep teams in control
while agents run the work

Every Agivant agent is production-ready from day one, observable and always on, with a human in the loop wherever judgment matters. Revenue and engineering teams continue to lead the business while agents run the process. Reusable assets built on Bolt and Google's Agent Development Kit strengthen each engagement and accelerate what comes next.
Control matters most where enterprise data already lives.

Scale securely on Google Cloud
with Marketplace-ready accelerators

Certified Gemini Enterprise engineers deploy on secure Google Cloud infrastructure, and Agivant's industry-validated accelerators are available directly through Google Cloud Marketplace. Standardized 90-day sprints turn ambition into measurable ROI, while enterprise-grade governance scales with every new workload. This is what an Amp'd enterprise looks like in production.
Scale securely on Google Cloud with Marketplace-ready accelerators

Accelerate business outcomes with production-ready
Gemini Enterprise solutions

Explore Agivant’s industry-validated Gemini Enterprise agent accelerators, live on Google Cloud Marketplace.
CPQ & revenue operations
Compress complex quoting cycles from days to minutes.

The challenge

Salesforce CPQ and Oracle CPQ environments often rely heavily on rules-based logic. Complex quote cycles can stretch from 3 to 7 business days and depend on scarce SME time, quietly wearing down margin along the way.

The solution

Multi-agent teams connect directly with the existing CRM. Revenue teams lead the business while agents run the process, compressing quoting cycles from days to minutes.

Meet the agent team

Deal manager: Orchestrates the full workflow and invokes every specialist agent.
Requirement parser: Extracts structured requirements from RFQs, transcripts, and emails.
Catalog scout: Runs semantic product discovery across SKUs and provides product recommendations.
Quote builder: Assembles quotes from products, pricing, discounts, and accounts.
Quote analyser: Delivers win-rate analysis for every quote.
Twin hunter: Analyzes across the industry to surface look-alike accounts.

Days → Minutes

Quoting cycle compression, start to finish
10% win-rate uplift · 30% more active selling time · Stronger first-time quote quality

Proof from production

Gemini Enterprise engagements already running, with outcomes measured in the environment they were built for.
Technology and internet

Agentic IT support operations

Agentic AI & AgentOps
Deep Dive

$20M+ projected annual capex saving from agentic IT support operations

Modeled on a support operations team of 120 or more. Agivant mapped an agentic IT support operation against the client's current cost base, projecting about 50% lower mean time to resolution and a 25 to 40% lower cost per ticket.

$20M+Projected annual capex saving
Technology and internet

Gemini-assisted Salesforce development

AI & ML Engineering
Deep Dive

User story to deployable Salesforce code in 2 hours, down from 8, with Gemini

Salesforce feature development ran eight hours from user story to deployable code. Agivant introduced Gemini-assisted development with automated test case and script generation, reaching 90% test-case automation within two weeks.

8 hrs to 2User story to deployable Apex code
Technology and internet

Enterprise MLOps on Vertex AI

MLOps & Scalable ML Platforms
Deep Dive

80%+ accuracy sustained a year after go-live across 5M+ transactions a month

Deep learning audio quality models had to hold accuracy in production at volume. Agivant built the MLOps platform with continuous drift monitoring and automated retraining and deployment pipelines, under full audit trail and version control.

80%+Accuracy sustained a year after go-live
Technology and internet

Customer call classification

AI & ML EngineeringMLOps & Scalable ML Platforms
Deep Dive

97% classification accuracy on test data, with 5M support calls a month scored in production

Two-way customer service calls carried signal nobody could read at volume. Agivant built classification models scoring five million calls a month in production, cutting processing time 50% and support hours by about 60%.

97%Classification accuracy on test data, across 32 audio metadata attributes
Technology and internet

$20M+ projected annual capex saving from agentic IT support operations

Modeled on a support operations team of 120 or more. Agivant mapped an agentic IT support operation against the client's current cost base, projecting about 50% lower mean time to resolution and a 25 to 40% lower cost per ticket.

$20M+Projected annual capex saving
Technology and internet

User story to deployable Salesforce code in 2 hours, down from 8, with Gemini

Salesforce feature development ran eight hours from user story to deployable code. Agivant introduced Gemini-assisted development with automated test case and script generation, reaching 90% test-case automation within two weeks.

8 hrs to 2User story to deployable Apex code
Technology and internet

80%+ accuracy sustained a year after go-live across 5M+ transactions a month

Deep learning audio quality models had to hold accuracy in production at volume. Agivant built the MLOps platform with continuous drift monitoring and automated retraining and deployment pipelines, under full audit trail and version control.

80%+Accuracy sustained a year after go-live
Technology and internet

97% classification accuracy on test data, with 5M support calls a month scored in production

Two-way customer service calls carried signal nobody could read at volume. Agivant built classification models scoring five million calls a month in production, cutting processing time 50% and support hours by about 60%.

97%Classification accuracy on test data, across 32 audio metadata attributes
Semiconductor and EDA

50% lower MTTR with an agent that recommends the fix and runs it on approval

Incident knowledge sat across tickets, documents, forums and runbooks. Agivant built an agent that runs root cause analysis automatically, recommends the fix and executes it on approval, with alert noise down 30%.

50%Lower mean time to resolution