Data quality. Evals. Governance. Integration. IP. The work that sits between a frontier model and a running production system is where most enterprise AI fails. We live there. Forward-Deployed Engineers on your ground from day one. 40+ AI use cases shipped across 25+ enterprises. Model-agnostic by design. Your tokens, traces, and IP stay under your roof.
We embed with your team to design, build, and harden AI systems end-to-end. Product-engineering rigor, senior bench, no offshore swap. 40+ use cases shipped across ML, agents, and data.
Multi-step, tool-using agents that reliably complete real work. Guardrails, evals, and observability built in from day one.
RAG pipelines, fine-tuning, prompt systems, and product-grade UX for LLM applications. Engineered for cost, latency, and quality.
Forecasting, ranking, classification, and recommender systems. From notebook to production with proper MLOps, monitoring, and retraining.
The foundation that makes AI actually work. Streaming pipelines, lakehouses, feature stores, and warehouses built to scale on Databricks, Snowflake, and cloud-native stacks.
Decision-grade analytics. Semantic models, governed metrics, self-serve dashboards, and the rare combination of business clarity and engineering rigor.
React front-ends. Scalable APIs. Event-driven services. Cloud infrastructure. We build the product surface that AI lives inside, not just the model.
Modernize legacy estates, re-platform data and apps to the cloud, and put AI in the path of real workflows. Strategy, architecture, and execution. One roof.
The data work frontier labs and applied-AI teams hand us. SWE-bench-style benchmarks. RLHF and preference data. Agent traces. Eval sets. Expert-led annotation. Calibrated, auditable, defensible.
Frontier labs run their evaluation programs through us. Enterprise AI teams hand us the data work that needs domain experts on screen, not crowd. Applied-AI groups inside Fortune 500s call us when a benchmark has to be defensible to a regulator.
The work: SWE-bench-style coding benchmarks. RLHF and preference data. Synthetic data. Eval sets. Agent traces. Multimodal annotation. About 500 people on staff, plus a wider talent bench when a program asks for more.
Twelve practices below. À la carte, or hand us a program.
Prebuilt blueprints and components that take production-grade agents from prototype to live in weeks, not quarters.
Custom autonomous agents that orchestrate workflows across your systems. Guardrails, traces, and evals from day one.
Model Context Protocol servers and tools that connect agents to your enterprise systems. No more one-off integrations.
Retrieval-augmented generation pipelines tuned for measurable accuracy at real production scale and latency.
Knowledge graphs plus retrieval. Contextual, explainable reasoning across complex enterprise data and entities.
Bespoke domain models with the data pipelines, evaluations, monitoring, and guardrails to keep them sharp over time.
Large-scale model training programs on enterprise-grade infrastructure when general-purpose isn't enough.
End-to-end reinforcement learning from human feedback. Annotator networks, reward modeling, and quality assurance loops.
Deterministic harnesses, trajectory grading, and custom benchmarks that turn "feels better" into measurable wins.
Governance, compliance, privacy controls, and observability for AI you can defend to a regulator on a Monday morning.
OCI-native packaging, versioning, and deployment of models with their data and dependencies. Reproducible across every environment.
Engagements designed so you can swap LLM providers without rewriting production. Your code, your tokens, your IP stay under your governance.
Deep working partnerships across the cloud, data, and AI providers shaping the next decade. Engagements move at integration speed, not procurement speed.
Recognised globally. Trusted by the brands that have to get it right.
Eight industries where we've shipped repeatedly. Pattern recognition across customers in the same vertical, plus the regulatory and data nuance each one demands.
Mainframe modernization, AI underwriting and credit-memo automation, risk, fraud, ops, model governance (SR 11-7, ECOA). We speak BFS and we speak regulator.
Advisor copilots, underwriting, claims, agent enablement, member experience. Plus the privacy posture to defend them all. RAG and GPT-4o deployed at thousands-of-advisors scale.
Predictive maintenance, supply chain, vision QA, factory-floor copilots. Built for the physical world's tolerances.
Frontier labs, security, dev tools, SaaS. From SWE-bench evals to RLHF programs to agent platforms.
Clinical, regulatory, R&D. HIPAA-grade pipelines, evidence-generation agents, and trial acceleration.
Personalization, creative automation, MMM, customer data platforms. Moving the metrics CMOs actually report on.
Network ops, contact centres, revenue management, loyalty. Agents that handle the long tail customers actually feel.
Citizen services, defence-adjacent AI, sovereign data residency. On-premise and air-gapped capable.
Legacy modernization. A greenfield vertical AI platform for complex lending. A production-scale advisor copilot. Three engagements, three different shapes of the same problem. All delivered with FDE pods on customer ground and under standard InfoObjects engagement terms (customer-owned IP, mutual NDA, model-agnostic).
Cogence.ai connected legacy code context, mainframe data, business rules, documentation, and operational knowledge into a single governed AI layer — with approval paths, human review, audit logs, and measurable modernization outcomes. The team moved from "we don't know what we have" to a planned, evidence-backed migration in weeks.
A governed AI layer for understanding legacy applications, dependencies, business rules, and modernization gaps. Searchable, traceable, reviewable.
No clear view of how legacy applications worked, where business logic lived, or which systems depended on each other.
Organised legacy code, documents, rules, and data flows into a searchable layer for review and modernization planning.
AI-assisted discovery and validation of business logic for modernization, migration readiness, and cloud transformation.
Business rules buried across legacy systems, documents, batch jobs, data flows, and operations. Slowing planning, raising risk.
Semantic search, rule retrieval, documentation workflows, and human-in-the-loop validation for clearer migration plans.
"Exceptional AI consulting partnership. The consultants were knowledgeable, collaborative, and quick to adapt. Communication was clear, consistent, and proactive throughout."
We built a vertical AI platform for a US financial-services partner serving community banks, credit unions, and SBA / USDA lenders. Underwriting, credit-memo drafting, document extraction, and examiner-ready compliance workflows — engineered with credit officers in the loop on every decision, and shipped with the governance posture regulators expect.
Agent-assisted underwriting and credit-memo drafting that show their work. Every claim cited back to the source document, every model output reviewable, every approval auditable.
Manual underwriting and memo drafting taking days per file, with inconsistent quality and limited audit trail for examiners.
Agentic workflows designed model-agnostically, with provenance, evals, and human approval baked into every step.
Structured extraction across the messy document set that drives complex commercial lending. Field-level confidence, citations, and reviewer queues for low-confidence cases.
Hours of manual data entry from heterogeneous document types. Every loan, every applicant, every renewal cycle.
Domain-tuned extraction pipelines integrated on top of existing loan-origination systems. No rip-and-replace. Eligibility screening for SBA 7(a) and USDA programs.
At a Fortune 500 US wealth-management and insurance firm, thousands of advisors and field staff were losing hours per case hunting through fragmented product documentation — driving SME bottlenecks, longer call queues, missed deal closures, and attrition. We built a retrieval-augmented copilot: cited answers from the entire product-knowledge estate, surfaced inside the advisor workflow.
Product documentation was fragmented across systems. Long case-handling times, deep SME dependency, gaps in product information that hit deal closure.
Reps couldn't find the right answer fast enough. Documentation lived in too many systems, and SMEs were the only reliable path through it.
Enterprise Search API feeding a Unity Catalog pipeline (landing, consume, and chunking delta tables) with a vector database powering semantic retrieval.
Higher attrition, larger call volumes, missed and delayed deal closures. All rooted in the product-information gap. The copilot closed it without replacing the workflow.
Adoption depends on context: a copilot that lives outside the rep's daily tools doesn't get used.
RAG copilot powered by GPT-4o and a tuned embedding model, surfaced inside the advisor's existing workflow. Every answer cited back to source documents.
Real quotes from senior engineering and product leaders at the companies we've worked alongside.
At an all-hands yesterday, our CTO mentioned Epsilon's pivot-to-product focus. When asked which products specifically, he called out three — all of them led by InfoObjects. For a team that was meant to be temporary, InfoObjects has delivered the top pieces of our product portfolio.
The InfoObjects solution was specifically called out by our President — so to the entire team, a well-earned "well done." Great leadership in driving this initiative, and meaningful contributions across the board.
I want to thank InfoObjects for all the hard work this year. I am proud of what this team has done in bringing this project to life for Rockwell's Managed Services business.
InfoObjects did a fantastic job getting onto the project work and pushing it forward. They have achieved greatness and deserve every bit of it.
Great job InfoObjects — please share our appreciation with all the team members. Your team did an amazing job on getting the solution in place.
I'm lucky to work with this team and execute on our goals as a product. The work we do will ultimately impact our business goals — and with this team in place, I'm sure we'll achieve it all.
We've built the data, infrastructure, and evaluation systems that go into training and improving today's foundation models. Coding benchmarks. RLHF pipelines. Large-scale labeling automation.
Curated repositories, gold patches, executable test harnesses, and agent-grade evaluation. For teams shipping coding agents and frontier models.
End-to-end reinforcement learning from human feedback. Annotator training, comparison interfaces, reward modeling support, and quality assurance loops.
Model-in-the-loop labeling that compounds: weak supervision, active learning, programmatic labeling, and human-AI hybrid pipelines that get faster over time.
Targeted post-training initiatives. Error mining, gap analysis, capability uplifts, and continuous evaluation that ties model changes to real outcomes.
Cogence.ai is our dedicated AI practice — bringing together our frontier data work, agentic AI engineering, and applied research into a single brand. When you engage InfoObjects on AI, you're working with Cogence.
Visit Cogence.ai →Most large consultancies are quietly handing the future of their relationships to the frontier labs they deploy. Plugging OpenAI or Anthropic straight into a client's stack with no isolation in between lets the lab see every workflow, fund your competitors, and ultimately route around you. The model layer is converging. Execution is the moat.
Our engagements are built the other way around. Model-agnostic by design — frontier when it earns its keep, fine-tuned and open-weight where it wins on cost, latency, or data residency. FDEs deploy into your environment, your code and IP stay under your roof, and we can swap a model provider on a Friday without breaking production on Monday.
Route the right work to the right model. Frontier, open-weight, or specialized fine-tunes. No rewrites. Switch providers when economics or governance demand it.
Forward-Deployed Engineers embed in your environment from day one. The trust, edge cases, and integrations that make enterprise AI actually work happen with people on the ground.
The curated knowledge under your AI is the IP, not the model. We design every system so the prompts, traces, evals, and tuned weights stay under your governance, not a lab's.
Problem-solving isn't finding answers. It's asking the right questions until you know what determines success, and how to get there. That instinct runs through every engagement.
We treat your problem like our product. Our team operates with the accountability of an internal engineering org, not the detachment of a vendor.
We architect for where the field is heading, not where it was last year. Foundation models, agents, and evaluation are moving targets. We plan for that.
Forward-Deployed Engineers embed in your environment from day one. The same senior team that designs the architecture writes the code that ships. Not partners selling juniors.
Conventional methods solve conventional problems. We're hired for the rest. We're not afraid to try the unobvious approach.
A senior bench with deep roots in data, AI, and product engineering. They lead every engagement from problem framing to production rollout.
Rishi brings over 25 years of experience in enterprise applications, analytics, and distributed systems. He is a published author of two books on Apache Spark and has contributed to top technical publications. Prior to founding InfoObjects, he was part of the algorithms and analytics team at Netflix. Rishi holds a Bachelor's degree from the Indian Institute of Technology (IIT) Delhi and has completed PhD-level coursework at Stanford University in probability, randomization, and advanced mathematics. He leads with a strong vision for ethical AI and data innovation.
Sudhir has over 25 years of experience in software engineering and building scalable, distributed systems. Before co-founding InfoObjects, he held leadership roles at multiple product startups focused on enterprise-grade architectures. He earned his Bachelor's degree from IIT Delhi and an MS in Software Systems from BITS Pilani. Sudhir has also completed PhD-level coursework at Stanford University in probability, randomization, and advanced mathematics. He drives the technology vision for GenAI and modern platform development.
Dave brings over 20 years of leadership experience in technology, consulting, and professional services. Before joining InfoObjects, he led VIRCON, a boutique IT consulting firm, and managed award-winning IT infrastructure teams. He holds a BS from Cal Poly, San Luis Obispo. Dave is active in community outreach with organizations like Habitat for Humanity, YearUp, and Computers4Kids. Passionate about responsible tech, he champions the ethical adoption of Generative AI and decentralized platforms to transform businesses and communities.
Utkarsh leads engineering with more than 23 years of experience across AI, GenAI, RAG, cloud platforms, and digital transformation. He has delivered large-scale, innovative solutions for clients like Pfizer, Western Digital, Publicis Groupe, and United Community Bank. A graduate of IIT Delhi, Utkarsh was a Co-Founder at Algorism (acquired by MGL) and at SM Macario Software. He blends deep technical insight with leadership in AI, having led global engineering teams across onshore and offshore delivery models.
We start with the questions you haven't asked yet. Define what success actually looks like and what blocks it today.
Focused, real artifacts. Running on your data, evaluated on your metrics. Decisions get easier when you can see them work.
FDEs embed in your environment. On the ground at your offices when it matters. Reliability, cost, latency, observability, retraining. We don't hand off prototypes; we ship systems that operate.
Evaluation loops, telemetry, and continuous improvement so the system gets better after we've gone. So does your team.
Headquartered in the USA, with senior delivery teams across four continents. Engagements run on your clock, your data residency, and your language.
Forward-Deployed Engineers, researchers, and applied AI experts. Work alongside an expert team on frontier problems for customers who can't afford to get it wrong. Fully remote, async-first, with optional time in our global offices.
Build and ship production GenAI systems. Agents, RAG pipelines, fine-tuning workflows, evals, guardrails. Embedded with customer teams as an FDE. You like writing the code that actually goes to prod.
Apply →Push frontier work. RLHF, evaluations, SWE-bench-style benchmarks, capability uplifts, model improvement programs. Bridge research papers and production code; ship results, not just findings.
Apply →Forecasting, ranking, classification, recommender systems. Take models from notebook to deployed service with proper MLOps. Pipelines, monitoring, observability, retraining.
Apply →Senior IC for our hardest engagements. Lead pods, design architectures, and own outcomes from problem framing to production rollout. You've shipped production AI at enterprise scale, and you love the customer-facing part.
Apply →// Don't see your role? Email support@infoobjects.com — we're always interested in exceptional senior talent.
We spend time on stage and on the floor at the events where our customers shape what's next. When it matters, we'll fly to your offices to listen first. Discovery, kickoffs, and executive read-outs are better in person.
Two days with senior leaders from 170+ banks and building societies. AI, infrastructure, payments, and the customer experience playbook for the next decade. We'll be on the floor talking AI-in-banking with the practitioners who are actually shipping it.
Event details →Europe's flagship AI summit, gathering the founders, researchers, and operators driving the next wave of applied AI. We'll be there meeting teams building real production systems. Come find us.
Event details →Want to set up a meeting at one of these events? Email the team and we'll find time.
We sign mutual NDAs before any technical conversation. No exceptions.
All code, models, weights, prompts, and artifacts created under our engagement belong to you.
Model-agnostic by design. Swap providers without rewrites. Your stack stays portable.
Customer data stays inside the customer environment. We never use it to train models we own.
Engagements designed for SOC 2, HIPAA, GDPR, and equivalent regional frameworks.
Tell us what you're trying to do, what's blocking you, and what done looks like. We'll respond within one business day with a real point of view — not a brochure.
Book a consultation →