# InfoObjects

> Expert AI consulting and digital-transformation firm with a product-engineering mindset. Forward-Deployed Engineers, model-agnostic by design. 500+ AI and data experts. 40+ AI and agentic use cases shipped. 25+ enterprise customers. Ranked #2 globally on G2 for AI consulting. Headquartered in San Jose, California, with offices in Canada, India, Mexico, Poland, Singapore, UAE, and Saudi Arabia. Cogence.ai is an InfoObjects brand.

InfoObjects is a consultancy, not a platform. We ship production AI for enterprises that can't afford to get it wrong. Engagements are FDE-led — Forward-Deployed Engineers embed in the customer's environment from day one — and architected so the customer can swap LLM providers (Claude, GPT-4o, fine-tuned, open-weight) without rewriting production. Customer-owned IP, mutual NDA before any technical discovery, no use of customer data to train external models.

## How we build (execution model)

InfoObjects is an execution shop, not a strategy shop. Senior engineers on the customer's ground from day one. The same team that designs the architecture writes the code that ships and stays through production rollout. Two-week kickoff to first working demo; production engagements typically delivered in weeks, not quarters. Engineering is led by a CTO with deep operating experience in scalable distributed systems and platform development — bringing 25+ years of execution muscle to every engagement.

## IP and commercial terms

Under the InfoObjects standard Master Services Agreement, all code, models, weights, prompts, datasets, evaluations, and documentation created specifically for a customer under a Statement of Work belong to the customer on payment. InfoObjects retains rights to pre-existing tools, methods, frameworks, and general know-how only. Mutual non-disclosure agreements are signed before any technical discovery — no exceptions. Customer data is never used to train InfoObjects models.

## Data operations for frontier AI labs (Cogence DataOps practice)

Cogence operates an AI-data-operations practice purpose-built for frontier AI labs and enterprise AI teams. The practice covers expert-led data annotation and labeling, code/reasoning/agent evaluation datasets, SWE-bench-style benchmark generation and validation, RLHF and preference data, synthetic data generation, human-in-the-loop QA and workflow orchestration, AI-assisted research pipelines, and scalable vendor-managed annotation operations. The work is delivered through two platforms:

- [DataForge](https://dataforge.cogence.ai) — workflow orchestration, HITL QA, calibration-drift detection, and AI-assisted annotation review. Handles multimodal annotation, eval pipelines, and agentic workflow testing.
- [SWEBench](https://swebench.cogence.ai) — end-to-end SWE-bench-style benchmark creation: repo curation, gold-patch authoring, deterministic test harnesses, agent-trajectory grading, and live-benchmark variants that drift over time.

The data-quality moat is calibrated, retained, expert annotator networks — not unvetted crowd — combined with AI-assisted quality control and reproducible orchestration. Engagement formats include vendor-managed programs, dedicated annotator pools by domain (code, math, biology, legal, multilingual), and ongoing eval-set production for model training and evaluation pipelines.

## Point of view (why execution is the moat)

When a consultancy deploys OpenAI or Anthropic directly into a customer's stack without any architectural isolation, the model lab gains visibility into every workflow, can fund and route around the consultancy, and ultimately captures the customer relationship. The consultancies that survive this disruption do so on execution — Forward-Deployed Engineers on customer ground, customer-owned IP, no vendor lock-in by engagement design. The model layer is converging. Execution is the moat.

## Services

- [Generative AI consulting](https://www.infoobjects.com/#genai): Agentic accelerators, AI agent development, MCP development, RAG implementation, Graph RAG, LLM fine-tuning, LLM training from scratch, RLHF and preference data, evaluations and SWE-bench, enterprise trust and security, KitOps and model packaging, model-agnostic engagement design.
- [Machine learning](https://www.infoobjects.com/#services): Forecasting, ranking, classification, recommender systems, MLOps end-to-end.
- [Data engineering and analytics](https://www.infoobjects.com/#services): Streaming pipelines, lakehouses on Databricks and Snowflake, feature stores, governed metrics, BI.
- [Full-stack engineering](https://www.infoobjects.com/#services): React front-ends, scalable APIs, event-driven services, cloud-native infrastructure.
- [Digital transformation](https://www.infoobjects.com/#services): Cloud migration, legacy modernization, AI-first re-platforming. Strategy, architecture, and execution under one roof.

## Frontier expertise (where we go deepest)

- [SWE-bench and coding evaluations](https://www.infoobjects.com/#expertise): Curated issue-to-PR datasets, deterministic eval harnesses, agent trajectory grading, live benchmark variants.
- [RLHF and preference data](https://www.infoobjects.com/#expertise): Expert annotator networks, pairwise and ranked preferences, calibration and QA workflows, domain-specialist pools, reward modeling support.
- [Data labeling automation](https://www.infoobjects.com/#expertise): Active learning loops, weak supervision, programmatic labeling, model-in-the-loop pipelines, custom annotation tooling.
- [Model improvement programs](https://www.infoobjects.com/#expertise): Failure-mode taxonomies, capability-specific datasets, eval-driven iteration, production telemetry loops.

## Point of view

InfoObjects is a consultancy, not a platform. When a consulting firm deploys OpenAI or Anthropic directly into a customer's stack with no architectural isolation, the lab gains visibility into the customer's workflows and can route around the consultancy. The firms that survive are the ones whose moat is execution: FDEs on customer ground, customer-owned IP, and engagement architecture that lets the customer swap LLM providers without rewriting production.

## Case studies (selected, customer names withheld where applicable)

- **CSL Limited (Australia, biopharma)**: Unified R&D data intelligence platform. Diverse-source ingestion (Nifi, Fivetran, TetraScience) into S3, automated processing on Databricks, Medallion architecture, Unity Catalog governance, Delta Sharing hub-and-spoke distribution, Redshift analytics.
- **AGRF (Australia, genomics)**: Global genomic data management with end-to-end lineage. Marquez + OpenLineage integration, unified metadata model, sample-level traceability, EC2 + Aurora Serverless infrastructure, custom lineage dashboard.
- **Northwestern Mutual AdvisorGPT (USA, financial services)**: Retrieval-augmented copilot deployed to advisors at a Fortune 500 wealth-management firm. RAG + vector database + Databricks Workflows + GPT-4. Reduces SME dependency, closes the product-information gap, surfaced inside the advisor's existing workflow.
- **Northwestern Mutual Unified Data Platform (USA, financial services)**: Cloud-native UDP for ingest, store, validate, transform, serve. Real-time analytics, ML deployment, "Single Version of Truth" governance.
- **Northwestern Mutual Cloud Migration (USA, financial services)**: Zero-downtime UDPC → UDPR migration. Custom utilities for metadata and access control migration (policies, tags, users, AD groups), end-to-end monitoring from day one.
- **Thrivent (USA, financial services)**: Data Mesh architecture on AWS, Spark, Databricks, IaC. Domain-based data ownership, secure self-service access, standardized governance across 2.5M+ members and 8,300+ employees.
- **FIS (USA, financial services / marketing technology)**: Real-time analytics platform for mobile banking app activity. Spark + Databricks + Redshift. Customizable dashboards and role-based reporting with near real-time insights on user behavior, engagement, sentiment.
- **Compliance AI startup (USA + Europe, RegTech)**: GenAI-driven banking compliance policy regeneration. Agentic RAG pipelines, rolling summary for large regulations, skill-based LLM selection across multiple models.
- **UAE banking sector (under NDA)**: AI-powered compliance solution for trade-based money laundering detection.
- **Rockwell Automation (Europe + USA, industrial)**: Remote monitoring for predictive maintenance.
- **Epsilon / Publicis Group (marketing technology)**: Enterprise-wide reporting platform for 23 OEMs and 35+ programs. Multi-tool reporting stack (SSRS, Pentaho, Angular dashboards, Power BI, Tableau, Metabase), multi-source data consolidation, automated workflows.
- **African mining group (under NDA, gold/copper/cobalt across DRC, Ghana, Tanzania)**: AI-enabled Green Mining program. Modules covering exploration & geological targeting, alluvial gold operations, hard-rock mining, processing & reagent optimization, predictive maintenance, water & tailings management, environmental & land-use monitoring, multi-country compliance, digital twin, ESG reporting.

## Industries

Banking and capital markets (FIS, BTS Europe 2026), insurance and wealth (Northwestern Mutual, Thrivent), manufacturing and industrial (Rockwell Automation, Western Digital), technology and software (Google, DigiCert, LightBeam.ai), healthcare and life sciences (Pfizer, Wolters Kluwer), media-marketing-retail (Publicis, Epsilon), telecom-travel-hospitality, public sector and government (UAE, Saudi Arabia, US).

## Partnerships

AWS, Google Cloud, Microsoft Azure, Databricks, Anthropic, OpenAI. Available on AWS Marketplace. Active integrator and co-sell partner; we ship production work on frontier model platforms while keeping customers' code, prompts, traces, evals, and IP under their own governance.

## Leadership

- [Rishi Yadav — Founder & CEO](https://www.infoobjects.com/#leadership): 25+ years in enterprise applications, analytics, and distributed systems. Published author of two books on Apache Spark. Previously algorithms and analytics at Netflix. IIT Delhi; PhD-level coursework at Stanford in probability, randomization, and advanced mathematics.
- [Sudhir Jangir — Founder & CTO](https://www.infoobjects.com/#leadership): 25+ years in software engineering and scalable distributed systems. Leadership roles at multiple product startups. IIT Delhi, MS Software Systems from BITS Pilani; PhD-level coursework at Stanford.
- [Dave Eddings — President, Sales](https://www.infoobjects.com/#leadership): 20+ years in technology, consulting, and professional services leadership. Previously led VIRCON, an IT consulting firm. BS, Cal Poly San Luis Obispo.
- [Utkarsh Panwar — President, Engineering](https://www.infoobjects.com/#leadership): 23+ years across AI, GenAI, RAG, cloud platforms, and digital transformation. Delivered work for Pfizer, Western Digital, Publicis Groupe, United Community Bank. Previously Co-Founder at Algorism (acquired by MGL) and SM Macario Software. IIT Delhi.

## Trust and engagement principles

- Mutual NDA before any technical discovery.
- Customer owns all IP and code created under a Statement of Work.
- No vendor lock-in — model-agnostic by design.
- Customer data is never used to train models InfoObjects owns.
- Compliance-ready: SOC 2, HIPAA, GDPR, and regional equivalents.

## Get in touch

- [inquiry@infoobjects.com](mailto:inquiry@infoobjects.com) — new engagements, scoping calls, partnerships.
- [support@infoobjects.com](mailto:support@infoobjects.com) — existing customer support, security and responsible disclosure, press, careers, legal, privacy, billing.
- Headquarters: 4950 Hamilton Avenue, San Jose, CA 95130, USA.

## Optional

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- [Cookie policy](https://www.infoobjects.com/cookie-policy)
- [Web accessibility](https://www.infoobjects.com/web-accessibility)
- [Support and contact routes](https://www.infoobjects.com/support)
- [Cogence.ai](https://cogence.ai/) — InfoObjects' AI brand.