Data engineering and advanced analytics AI can depend on

We build pipelines, semantic layers, knowledge graphs and vector retrieval so agentic systems have reliable ground truth from day one. One team owns the pipelines and the analytics that run on them, so the mechanical work runs on its own and your team spends its time where judgment is actually needed.

80%

Annotation automation

90%

Records/month

50%

Processing time cut

90%

Quality confidence

Data platform teams

Analytics teams

Data operations

Ground truth and annotation teams

Clean data, confident AI

AI-ready data platforms and semantic layers

Semantic layers and vector retrieval built for agent access
One source serving models and analytics from the same ground truth
Observability, CMDB, identity and ticket history consolidated into one warehouse
Context an agent can act on from its first day in production

Data quality, lineage and traceability

Quality checks built into the pipeline itself
Lineage tracked with Unity Catalog, Microsoft Purview or AWS Lake Formation
Silent failures surfaced before a report reaches the business
Business reports on time, every cycle

Conversational analytics for leadership

Conversational analytics running on a semantic layer
Cross-function views assembled from one consolidated source
Reporting adoption measured by how often the numbers get used
Confidence in agent output built on numbers leadership already trusts

Real-time pipelines and streaming

Streaming pipelines for live operational signals
Batch and real-time paths engineered as a single system
Anomaly detection that stops bad data at source
Auto-recovery within minutes on pipeline failures

Advanced analytics and knowledge graphs

Predictive and prescriptive analytics traceable to the source records
Knowledge graphs connecting entities across functions and systems
Forecasts that hold their accuracy as data volume grows
Relationships an agent can reason over without a person assembling them first

Data operations and annotation at scale

Annotation pipelines automated across the bulk of task volume
Records processed continuously at sustained accuracy
Quality scored on every record processed
Processing time on ground-truth work cut sharply

AI-ready data platforms and semantic layers

Semantic layers and vector retrieval built for agent access
One source serving models and analytics from the same ground truth
Observability, CMDB, identity and ticket history consolidated into one warehouse
Context an agent can act on from its first day in production

Data quality, lineage and traceability

Quality checks built into the pipeline itself
Lineage tracked with Unity Catalog, Microsoft Purview or AWS Lake Formation
Silent failures surfaced before a report reaches the business
Business reports on time, every cycle

Conversational analytics for leadership

Conversational analytics running on a semantic layer
Cross-function views assembled from one consolidated source
Reporting adoption measured by how often the numbers get used
Confidence in agent output built on numbers leadership already trusts

Real-time pipelines and streaming

Streaming pipelines for live operational signals
Batch and real-time paths engineered as a single system
Anomaly detection that stops bad data at source
Auto-recovery within minutes on pipeline failures

Advanced analytics and knowledge graphs

Predictive and prescriptive analytics traceable to the source records
Knowledge graphs connecting entities across functions and systems
Forecasts that hold their accuracy as data volume grows
Relationships an agent can reason over without a person assembling them first

Data operations and annotation at scale

Annotation pipelines automated across the bulk of task volume
Records processed continuously at sustained accuracy
Quality scored on every record processed
Processing time on ground-truth work cut sharply

The amp'd way

Consolidate the sources

Observability, identity, ticket history and business data are brought into one warehouse worth building on.

Engineer the pipelines to hold

Streaming and batch paths are built together, with anomaly detection and recovery inside the pipeline.

Add the analytics and retrieval layer

Semantic layers, vector retrieval and knowledge graphs make the same ground truth readable by agents, forecasts and analysts alike.

Make the data traceable

Quality checks and lineage are built in, so every number traces to its source and the business trusts what it reads.

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

Vendor briefings in under five minutes, across billions in annual spend

Analysts spent 15 to 20 hours a week per vendor assembling briefings by hand across five disconnected systems. Agivant built VIBE on the client's own large language model, pulling live spend, market signals and financial health into one view.

Under 5 minTo produce a vendor briefing, previously 2 to 3 business days

Semiconductor and EDA

Pipeline maintenance down 80%, with auto-recovery in minutes

Pipeline maintenance took 20 hours a week and failures meant manual restarts around three hours long. Agivant rebuilt the pipelines with automatic recovery, bringing maintenance to four hours a week and ingesting 250GB+ of new data each week.

80% downPipeline maintenance, 20 hours a week to 4