Anyone can prompt an app into existence in a weekend. Keeping it alive under real users, real attacks, and real audits is a different sport. That sport is ours.
Insym is an independent AI lab standing on twelve-plus years of enterprise engineering: certified Meta Tech Providers who built hosting infrastructure serving tens of millions of visitors a month, before AI reached the market. Now we bring that discipline to frontier models: integrations, pipelines, agents, and rescues that survive production.
That's the half everyone quotes. The other half: adopting AI badly digs a deeper hole than not adopting it at all. Three ways companies are losing the AI decade right now:
While your evaluation committee schedules its next meeting, a competitor's agents are answering their tickets, writing their code, and closing their books. You won't feel it this quarter. You'll feel it when their prices drop and their release notes triple.
A thin interface over someone else's API: priced per token, repriced overnight, deprecated without apology. That's not an AI strategy. That's a subscription with your logo on it.
Someone prompted an app into existence, and it almost works. No tests, no security review, no docs, no one who can explain it. Now it's load-bearing, every new feature is a coin flip, and the person who prompted it left in March.
All three are fixable, and all three compound. What costs a sprint to fix today costs a roadmap next year. We take stuck personally.
Six ways in. Every engagement is run by the people who build it.
Frontier models, wired into software you already run.
Claude, GPT, GLM, Kimi: in your product, your workflows, your stack. Our routing harness fronts them all, blending a council of models per request for better answers at lower cost. The model is 10% of the job. We build the other 90: context, guardrails, fallbacks, observability.
Your AI wrote it. Nobody tested it. We fix both.
That AI-generated codebase isn't a write-off. It's a first draft. We audit, test, harden, and document it until it's production-grade, then install the harness: evals, reviews, CI gates. The next thousand AI-written lines come out clean. We've shipped with AI pair-programmers since day one. We know exactly where they cut corners.
Agents that survive contact with production.
Harnesses, loops, graphs, memory, approval gates. We design agents like distributed systems, with budgets, checkpoints, and recovery, because that's what they are. Demos loop forever. Ours terminate, with logs you can audit.
Your AI is only as smart as what reaches it.
Ingestion, cleaning, schemas, retrieval, knowledge graphs. Sometimes that means a vector store. More often it means something simpler that ships. Residency, retention, and audit trails are designed in, not bolted on. We build the plumbing first. It's where most AI projects die.
When no API model fits, we shape one that does.
Fine-tuning, distillation, quantized deployment, all the way down to from-scratch pretraining. We've done the full run: Aura, our 9B reasoning model, went from raw data to a model you can run on one GPU. Most integrators have never trained one.
$750/hour. Worth every fucking penny.
One call. We take your stack apart, show you where money is burning and where AI pays, and leave you a blunt, prioritized blueprint: automate this, build that, ignore the rest. It occasionally stings. It reliably works.
Every Insym build runs on our routing harness. It convenes the frontier as a council, remembers like a colleague, and wraps the whole thing in the discipline enterprise data demands.
Each request is routed, debated, and fused across Claude, GPT, GLM, and Kimi. Differential arbitration plays each model to its strength, so answers beat any single flagship, at lower cost than running one for everything.
Durable, structured memory across sessions, tools, and teams. Your agents compound what they learn instead of restarting at every prompt.
Our proprietary sealing layer encapsulates sensitive data before any request reaches an external provider and restores it on return. Providers compute. They never read.
GDPR, CCPA, and the rules of your industry live in the pipeline, not the slide deck: data residency, retention, audit trails, and prompt-injection defenses on by default.
Data, pretraining, post-training, deployment: we ran the entire pipeline ourselves, not a fine-tune of someone else's weights.
Prototypes in days, production in weeks. We run on the same agentic harnesses we sell, so the tooling sharpens with every engagement.
Claude Agent SDK and MCP. OpenAI's agent stack. GLM and Kimi when the job wants open weights on your own metal. Our routing harness runs them as a council: differential arbitration and model fusion that beats any single model on quality, at lower cost.
Twelve-plus years of enterprise engineering. Certified Meta Tech Providers. We built and ran hosting infrastructure serving tens of millions of visitors a month, all before AI reached the market.
9B reasoning model, trained from scratch. Runs on one GPU you own.
Agent-based behavior simulation: ten thousand simulated users rehearse a decision before you ship it.
Recurrent-depth architecture: small models that think in loops to reason like far larger ones.
Continuously-learning system on simulated spiking neurons. Air-gapped until the safety story matches the capability story.
INTERNAL · NOT FOR SALEThe modern AI economy is a landlord economy: you bring the data, the workflows, and the expertise, and rent back intelligence that can be repriced, rate-limited, or deprecated on someone else's schedule. Fine for experiments. Fatal as a foundation.
Insym exists to put you on the right side of both ledgers: ahead of the competitors who move slower, free of the landlords who own your stack. We fix what's stuck, wire in what works, and build what doesn't exist yet, so the intelligence layer of your company answers to you. Ownership isn't ideology. It's just good engineering.
Not the demo that impresses. The invoice thing that's been broken since June. Boring problems hide the best ROI in your company.
Grep and a well-designed schema beat a four-stage RAG pipeline more often than anyone admits. We deploy the boring thing that works and save the exotic tech for problems that have earned it.
Senior engineers with agentic tooling and no hierarchy outrun departments. The speed comes from removing drag, not from cutting corners.
We measure ourselves in running systems, not roadmaps, decks, or pilots that die in staging.
The weights, the deployment, the pipeline: own at least one. If your core intelligence lives entirely on someone else's terms, it isn't yours.
A stalled pilot. A codebase nobody understands. A model bill that doubled overnight. A competitor that's suddenly shipping twice as fast. Email the engineers who'll build the fix. The first reply comes with opinions, not a sales deck.