AI agent engineering
Function-calling agents with bounded autonomy, retry logic and explicit state tracking — built to run, not just demo.
Industries · AI
We build AI people trust.
Production AI systems built to handle real inference load, not demo traffic. Agents, RAG, eval harnesses — plus the developer marketing and analyst relations that cut through a market full of demos.
Corum8 builds production AI systems — agents, RAG pipelines, fine-tuned models and inference infrastructure — and runs the developer-first marketing, technical content and analyst relations that separate a real launch from the wall of me-too AI announcements. 40+ AI systems in production, serving 200M+ inferences monthly, across teams that need both the engineering and the credibility to be taken seriously.
What's included
What we build and run for AI teams
LLM pipelines & RAG systems
Retrieval quality treated as the product, evaluated independently from generation and re-ranked properly.
Model fine-tuning & eval harnesses
Golden-dataset evaluation built before the prompt, so quality regressions get caught, not discovered by users.
Inference infrastructure
vLLM, TensorRT and Triton deployments for teams that need self-hosted or sovereign inference.
AI product launch programs
Positioning and go-to-market built to differentiate a real system from the wall of demo-stage AI announcements.
Developer marketing & docs
Documentation and technical marketing that developers actually trust — written by people who understand the stack.
Technical content & AI research posts
Research-grade writing that builds credibility with a technically literate audience, not marketing fluff.
Analyst relations
Positioning and briefing support for category conversations with CB Insights, Gartner and comparable analyst firms.
Is this you?
Signals you need an AI team that ships and tells the story
You don't need all of them. One is usually enough to justify the call.
Your prototype is stuck in a notebook
It works in a demo, but nobody's put it in front of real users because there's no eval harness behind it.
Your launch looks like everyone else's
You're one of a hundred AI announcements this month and nothing in your positioning differentiates the real system underneath.
Developers don't trust your docs yet
Your technical audience needs credibility signals your current content doesn't provide.
You want a category conversation, not just coverage
You need analyst relations and framing that puts you in the right comparison set, not a press release nobody reads.
Inference cost is unexplained
You're paying real money in model costs monthly and nobody can point to which feature drives most of it.
Legal or procurement is asking for proof
Someone internally wants a verifiable evaluation story before the product goes near real users.
Sectors
Where we work across AI
The product differs, the discipline doesn't.
Enterprise AI Ops & Agents
Cross-tool agents automating real internal workflows at scale.
AI-Native Consumer Products
Consumer features where AI is now a baseline expectation, not a novelty.
Developer Tools & AI Infrastructure
Inference platforms, orchestration tools and AI-native developer products.
High-Stakes AI (Fintech, Healthcare, Legal)
Systems that need an evaluation story that satisfies your reviewers, not just users.
AI Trading & Market Intelligence
Signal extraction and anomaly detection over market and on-chain data.
On-Chain & Web3-Native Agents
Agents that read chain state and execute bounded, auditable on-chain actions.
Enterprise & Government AI Pilots
AI integration for operations teams inside procurement-heavy organizations.
AI Category Creation & Positioning
Founders defining a new category who need analyst relations from day one.
Process
How an AI engagement runs, in practice
- 01
Define eval and positioning together
The golden dataset and the market positioning get scoped in the same conversation — engineering and story, not sequential.
- 02
Build production AI
Model routing, retrieval or agent orchestration engineered against the eval set, not a demo script.
- 03
Launch with developer-first content
Documentation, technical posts and launch coverage built for a technically literate audience that can smell hype.
- 04
Run analyst relations and re-evaluation
Category-conversation briefings alongside quarterly model-upgrade and eval reviews — a continuous operation, not a launch event.
Case studies
AI work we've shipped
An enterprise agent platform and a developer-infrastructure launch, each pairing the eval work with the story that got it taken seriously.
Production agent platform launched with analyst-ready positioning
An enterprise-ops startup had a working agent prototype but no eval story and no positioning that separated it from a crowded AI-agent category. Building the golden-set evaluation harness alongside category-defining launch messaging and analyst briefings got the platform into serious enterprise-buyer conversations instead of a generic 'AI agent' comparison set.
Inference platform launch backed by technical content that developers actually shared
An AI infrastructure startup needed developer marketing that wouldn't get laughed out of the room by a technically sophisticated audience. Research-grade technical posts and documentation written by people who understood the actual inference stack got organic developer sharing that paid marketing alone couldn't buy.
40+ AI systems in production
200M+ Inferences served monthly
1,100+ Projects delivered since 2016
50+ Awards won
Why Corum8
Why AI teams work with us
Engineers who ship, not just theorize
The team building the eval harness has shipped production AI since the GPT-3.5 era, not just read about it.
Developer marketing that doesn't insult developers
Technical content written by people who understand the stack — credibility a technically literate audience can actually detect.
Analyst relations that reach the right rooms
Positioning and briefing support for CB Insights, Gartner and comparable category-defining conversations.
One team, engineering and story together
The eval harness and the launch narrative get built in the same conversation, not handed off after the fact.
A decade of client relationships to draw on
10+ years across Web3, fintech and enterprise clients now applying the same discipline to AI products.
Honest about what AI can and can't do
We build evaluation stories that hold up under scrutiny, not marketing claims that overreach the technology.
What drives scope
What drives scope and budget on an AI engagement
The decisions that shape cost and timeline happen before any model gets called.
Model sovereignty
Third-party API vs private or on-prem inference — each layer adds engineering and operational overhead.
Evaluation rigor
Vibes-based quality is cheap. A golden-set eval harness with CI/CD for prompts is a system of its own.
Positioning ambition
A quiet feature launch is one scope. Category-defining positioning with analyst relations is a materially bigger one.
Developer trust requirements
A consumer feature needs less technical credibility than a developer-facing infrastructure product.
Scrutiny level
Consumer AI is lightest. AI in a high-stakes category needs a defensible evaluation story built to survive legal review.
Scale target
An internal tool for a small team is a different build and launch from a consumer feature aimed at hundreds of thousands of users.
FAQ
Questions worth a direct answer
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A serious AI agency builds production AI systems — agents, RAG pipelines, evaluation harnesses — and runs the developer-first marketing and analyst relations that separate a real launch from a demo announcement. Most AI marketing suffers because the underlying product isn't production-grade; most AI engineering suffers because nobody built a credible story around it. Corum8 does both under one roof.
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Cost is driven by model sovereignty, evaluation rigor, positioning ambition, developer-trust requirements, scrutiny level and scale target. A consumer feature with light evaluation is a different budget than a high-stakes AI system with a defensible eval harness and analyst-relations-grade positioning.
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Yes — through category-defining positioning grounded in what your system actually does, not generic AI marketing language. The differentiation usually comes from being specific about real capability and real evaluation results, which most AI marketing avoids because the underlying product can't back it up.
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No — we work with AI-native startups, and with fintech, Web3 and enterprise clients adding AI capabilities to an existing product. The engineering and positioning requirements differ by starting point, but the discipline of building the eval story alongside the launch story applies either way.
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Model routing and system architecture, evaluation harness design, developer-facing documentation and technical content, launch positioning, and analyst relations support where category conversations matter. Engagements scope to the specific product and audience — a developer-infrastructure launch and an enterprise product sold into legal review carry very different scopes.
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By building the evaluation infrastructure and the marketing content together, so every claim in the launch materials traces back to a specific, verifiable result. Institutional buyers and their legal teams spot overclaiming fast, and one unsupportable number costs more trust than three good ones earn. The credible path is specific, defensible measurements against a dataset you can show them — not generic AI marketing language.
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Yes — positioning and briefing support for conversations with analyst firms like CB Insights and Gartner is part of the practice for teams trying to establish a new category. This works best when the underlying product genuinely earns the category claim, not as a positioning exercise layered on top of a generic feature.
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We scope to the piece that unblocks you soonest, and grow from there. Sometimes that is the evaluation harness, sometimes the launch narrative, sometimes both together because the claims depend on the measurements. If the need is genuinely narrow — one integration, one placement — we will scope it narrow and price it that way. The combined engineering-and-marketing engagement earns its value when the product and its story need to move together, and we will tell you plainly which situation you are in before anything starts.