SynaLinks is an applied research team as much as an engineering one. We write the frameworks AI systems are built with, and we test our own ideas the way we ask you to test yours: with controlled, reproducible experiments, published alongside their code.
Does an AI system hold up over time? How do you improve one when you don't own the model? How do you prove an answer is correct, and not merely plausible? These questions get settled in production, at our clients' sites, and vendor literature doesn't answer them: it sells.
So we treat them as research questions. We state a hypothesis, build the experiment that could refute it, measure, and publish the result. It's slower than an opinion piece, and it's the only thing that earns us the right to tell you, afterwards, what will work for you.
Every one of these lives in our open-source repositories, where anyone can read the code. This isn't a roadmap of intentions: it's what we work on.
Improving an AI system without retraining a single weight: its instructions and examples are optimized against a measurable reward, the way you'd train a model. It's what makes continuous improvement possible when you don't own the model.
How you know a system answers correctly, and that it hasn't degraded since yesterday. Synthetic datasets, automated judges, agent metrics, and regression testing when a model is swapped underneath you.
Constraining a language model with structure and logic: outputs that are valid by construction, reasoning you can inspect, and conclusions that trace back to their premises rather than to a statistical hunch.
Turning documents into graphs a system can use, then querying those graphs: local search for precise questions, global search for questions that span an entire corpus.
An agent that writes and runs code is useful, and dangerous. We work on confining it: filesystem isolation, restricted system calls, controlled network access, capabilities granted explicitly rather than by default.
Pipelines where the model produces its own examples, the symbolic layer verifies them and discards what doesn't hold, and only what survives feeds the next step. The goal: getting better at a domain without pulling your experts into every iteration.
Most published "benchmarks" actually compare two systems that differ in ten ways at once. These are the rules we hold ourselves to so our numbers mean something.
We start from a question a number can answer, chosen before anything runs. A demo shows what works; an experiment tells you how much.
Same task, same train, validation and test splits, same models, same judge. Everything is held constant except the thing being measured, or the comparison measures something other than what it claims.
Each experiment fits in one runnable script, uses local models wherever possible, and keeps its full run logs. You can re-run it yourself.
Including when the result doesn't flatter us. A method that adds nothing on a given task is useful information, and burying it would make everything else unverifiable.
We run applied research with industrial teams and research labs: one identified technical blocker, an evaluation protocol built together, and a result you keep. The code we produce stays yours, and whatever we publish is agreed with you upfront.
If you have a question nobody around you can settle, we're interested: write to us, even if it's still roughly phrased.
Our research serves our clients first. The starting point is the same: an AI-readiness assessment, fixed scope and fixed price.
