AI Engineering & Advisory
AI systems that survive production.
Eximo advises teams on where AI is worth the investment, then builds the parts that make it work: data ingestion and normalization pipelines, retrieval-augmented generation, multi-agent orchestration, and secure agent communication. Legacy .NET modernization when you need it.
Where we go deep
What Eximo does
Advisory to decide what is worth doing, and an engineering team to build it: ingestion, retrieval, orchestration, agent security, and the tools on top.
AI Engineering & Advisory
For organizations ready to put AI into production, not just pilot it.
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AI Strategy & Advisory
A structured read on where AI creates real leverage for you, what to build first, what to skip, and how success gets measured. Delivered as a roadmap you could hand to any team.
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Data Ingestion & Normalization Pipelines
Turn PDF, CSV, JSON, and scanned images from many sources into one canonical, queryable schema, with embeddings generated on ingest so retrieval has a reliable foundation.
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Retrieval-Augmented Generation
RAG that holds up outside the demo: hybrid retrieval, chunking strategy, re-ranking, and an evaluation harness that measures retrieval quality on every change.
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Multi-Agent Orchestration
Production multi-agent systems: shared memory that is safe to share, one consolidated tool-call trace, and explicit handoff rules. Framework-agnostic.
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Secure Agent Communication
Let agents query across systems and coordinate over a narrow, audited channel: sandboxed policy enforcement, default-deny egress, and defenses against prompt injection and privilege escalation.
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AI-Powered Internal & Marketing Tooling
Agentic tools that replace manual, judgment-heavy work (reporting, lead qualification and routing, content generation, document triage) wired into the systems you already run.
Legacy .NET Modernization
For teams on aging .NET or VB.NET systems with mounting security and technical debt, including live migrations of systems serving hundreds of thousands of users.
Talk about modernizationFull Migration Projects
Legacy .NET Framework and VB.NET to modern .NET 8+, with a secure service layer that keeps the existing frontend live through cutover.
2-Week Modernization Sprint
A rapid assessment with a working prototype and migration roadmap. A low-risk way to see the value before a full project.
Emergency Legacy Rescue
Production crises and compliance deadlines. Fast diagnosis, stabilization, and a clear remediation path.
Patterns from the work
Anonymized engagement patterns across retrieval, data unification, agents, secure communication, and modernization.
A retrieval layer teams could actually trust
An organization had documents spread across formats and systems, and every AI feature re-parsed source files its own way. Answers were inconsistent and no one could say what was in the index.
- One shared retrieval layer for every downstream feature
- Retrieval quality tracked as a number, not a vibe
- New document sources onboarded without breaking existing answers
Dozens of inconsistent exports into one queryable schema
Source data arrived as PDF, CSV, and JSON from many providers, each formatted differently, with the same concepts represented five different ways. Nothing could be queried across sources.
- All sources queryable through one schema
- Format changes quarantined instead of silently corrupting data
- Models and dashboards insulated from provider-side changes
A multi-agent prototype made fit for production
A working multi-agent demo fell over under real traffic: agents corrupted each other’s state when run concurrently, and a bad run was nearly impossible to debug.
- Concurrent agents no longer clobber shared state
- Bad runs diagnosable from one trace
- Handoffs deterministic and bounded
Agents querying across systems without opening the doors
A design called for agents on different systems to query each other’s data and coordinate, but connecting them directly would have meant broad, standing network access between sensitive environments.
- Cross-system agent queries without broad network access
- Every agent action attributable and policy-checked
- Prompt injection and confused-deputy paths closed off
A legacy backend migrated with the frontend still live
A platform ran on a deprecated VB.NET backend with dead TLS dependencies and mounting security debt, serving hundreds of thousands of user accounts that could not go offline.
- Hundreds of thousands of accounts secured with MFA
- Deprecated TLS dependencies resolved
- No frontend downtime during the migration
A proven methodology for transformation
Our phased approach minimizes risk while maximizing continuity. We work alongside your teams to ensure knowledge transfer and long-term success.
Discovery & Assessment
Comprehensive analysis of your current architecture, processes, and business requirements. We identify risks, opportunities, and define success metrics before writing a line of code.
Strategic Roadmap
A detailed implementation strategy with phased milestones, resource requirements, and risk mitigation plans, aligned with your business objectives, not just technology preferences.
Phased Implementation
Execution in controlled increments with continuous validation. Production systems stay operational. Teams aren't left in the dark.
Validation & Optimization
Rigorous testing, performance optimization, and security hardening. We validate against defined success criteria and set you up for long-term maintainability.
Why Eximo
- Low-risk entry: See working results before committing to a full engagement
- Advisory and delivery: Strategy that comes with the team to execute it, not a slide deck
- Fast turnaround: Prototypes in weeks, not quarters
- Incremental delivery: A staged approach with clear milestones that limits business disruption
- Security built in: Agent isolation and data-boundary discipline from research, not bolted on later
Who runs Eximo
Joseph Call, Founder
Joseph founded Eximo to give teams a straight answer on AI: what is worth building, what is not, and how to ship the part that is. His background is in agentic systems, retrieval, and the security of agents that communicate with each other. That work continues through an affiliation with an AI research lab.
Eximo is an AI engineering and advisory firm. We work as a consultancy: an engagement is staffed with the people it needs (data engineers, ML and platform engineers, and .NET specialists) and scoped so you get an outside read before you commit to a build. The through-line is production systems that hold up, not proofs of concept that stall.
Advisory first
Most engagements open with an assessment: where AI actually creates leverage for you, what to build first, what to leave alone, and how success gets measured. You can stop there with a roadmap, or keep going into delivery.
A team scoped to the work
Eximo staffs each project with the specialists it needs (data engineering, ML and platform engineering, retrieval, and .NET modernization) rather than billing a fixed roster. The engineers who advise are the ones who build.
Research-backed on security
Our approach to agent systems draws on active research into secure agent-to-agent communication: sandboxed policy enforcement, and defenses against prompt injection, tool misuse, and privilege escalation. It is applied to client work where data sensitivity matters.
Common questions
What does Eximo do?
Eximo advises teams on where AI is worth the investment, then builds the systems that make it work: data ingestion and normalization pipelines, retrieval-augmented generation, embeddings and vector search, multi-agent orchestration, and secure agent-to-agent communication. Legacy .NET modernization is available as a separate service.
Is Eximo an advisory firm or a build shop?
Both, deliberately. Most engagements start with an assessment and a roadmap you could hand to any team. If you want Eximo to build it, the same engineers who advised you deliver it.
How is an engagement staffed?
Eximo works as a consultancy. Each project is staffed with the specialists it needs (data engineering, ML and platform engineering, retrieval, and .NET modernization) and scoped to the work rather than billed as a fixed roster.
How does Eximo handle sensitive data and agent security?
Our work on agent systems draws on active research into secure agent-to-agent communication: sandboxed policy enforcement, default-deny egress, and defenses against prompt injection, tool misuse, and privilege escalation. Retrieval and evaluation are designed to keep data inside your boundary.
How do engagements start?
Usually with a small, bounded first step: a scoped assessment or a prototype of one narrow piece, so you get an outside read before committing to a full build.
Start the conversation
Whether you're exploring AI integration or need to get off a legacy system, let's talk through what makes sense for your organization.