AI and ML engineering built for the domain and tuned for results

We build RAG systems, predictive models, AI-powered assistants and inference services, engineered to spec and measured on business value. Each one is tuned to your domain and measured on a number the business already watches. The repeatable analysis moves to the system, and your team spends its time where judgment is actually needed.

10x

Content velocity

90%

Test-doc automation

50+

Languages, zero lag

100%

QA coverage

Data science team

Product engineering

Domain specialists

Application teams

AI strategy and architecture

Model, data and serving choices mapped to the outcome they move
A sequenced build plan with the first release named up front
Architecture chosen on fit to the domain
Success measured on a business number agreed before engineering begins

Model customization and fine-tuning

Fine-tuning and adaptation on enterprise-specific data
Domain benchmarks agreed and tested against before release
Accuracy held steady well past launch
Retraining pipelines that keep the system current as data shifts

RAG and decision intelligence

Retrieval grounded in current, trusted enterprise sources
Reasoning steps recorded and open to audit
Knowledge graphs connecting entities across functions
Proprietary context used with IP held inside a secure enclave

AI-powered assistants

Support, voice and document workloads served continuously
Quality attributes scored automatically on every interaction
Root cause analysis carried inside the pipeline itself
Multilingual systems standing up across regions from a single build

Inference services and enterprise integration

Native integration with enterprise data, identity and services
Inference engineered for sustained production load
Text, voice and document workloads on one architecture
Accuracy monitored so it holds as volume climbs

Legacy code understanding and rebuild

Legacy business logic read and narrated for engineering review
Domain-specific code conversion automated across large codebases
Specialist attention spent on validating the result
Multi-year rewrite programs compressed to a fraction of the original plan

The amp'd way

Fix the target

The business number comes first, with the workload, the volume and the tolerance for error set alongside it.

Ground it in your domain

Data, retrieval and evaluation benchmarks are built from the enterprise sources your people already trust.

Build and validate

The system is tuned on your data and checked against domain benchmarks before it goes live.

Tune in production

Drift is monitored and retraining runs on a pipeline, so accuracy holds as traffic and data shift.

Client success in production, at scale

See how Agivant engineers real business value for the autonomous enterprise, across several industries

See more client stories

Technology and internet

Release-day product documentation

Data Engineering & Advanced AnalyticsAgentic AI & AgentOps

Deep Dive

AI-ready product documentation published on release day across every supported version

Documentation lagged behind releases and sat outside the product's own structure. Agivant built an automated publishing workflow with AI-assisted review, so documentation ships with each release in a structured layer that search can read.

Release dayDocumentation published with the product release

Technology and internet

AI-assisted documentation workflow

AI & ML Engineering

Deep Dive

Faster, consistent technical documentation through an AI-assisted publishing workflow

Documentation for fast-moving developer products and APIs ran as a largely manual process. Agivant standardized authoring and review into an AI-assisted workflow with approval gates before anything reaches production.

ConsistentQuality, with documentation standards enforced automatically

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

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

AI-ready product documentation published on release day across every supported version

Documentation lagged behind releases and sat outside the product's own structure. Agivant built an automated publishing workflow with AI-assisted review, so documentation ships with each release in a structured layer that search can read.

Release dayDocumentation published with the product release

Technology and internet

Faster, consistent technical documentation through an AI-assisted publishing workflow

Documentation for fast-moving developer products and APIs ran as a largely manual process. Agivant standardized authoring and review into an AI-assisted workflow with approval gates before anything reaches production.

ConsistentQuality, with documentation standards enforced automatically

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

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