Synops is an AI software company. We design, build, and run AI products, plus the platforms, data, and infrastructure that make them accurate, fast, and accountable. Then we stay on to keep them that way.
- Request
A user asks a question in plain language.
- Retrieve
Pull the relevant sources and embeddings.
- Computebefore the model
Derive the facts and numbers deterministically.
- Reason
The model works from the given facts, not guesses.
- Verify
Evals and guardrails gate the answer.
- Respond
Grounded, cited, and traceable to a source.
One team across the whole stack.
Most builds need more than one discipline. We cover the surface so the seams between them stay our problem, not yours.
Applied AI & LLM systems
Retrieval, evaluation suites, and grounding pipelines so a feature stays accurate and auditable, with every number computed before the model sees it.
Data & retrieval
The layer AI reasons over: Postgres-first models, embeddings, and pipelines you can actually reason about, so every answer traces back to a source.
Backend & distributed systems
Go services, event pipelines, and APIs designed around the guarantee first (ordering, consistency, durability), then made fast inside it.
Web & mobile products
Next.js on the web, Flutter on mobile. Product surfaces that put the AI in front of a user and stay fast as the feature set and the team both grow.
Cloud & infrastructure
Terraform and Kubernetes, provisioned to be reproducible and observable. Infrastructure chosen for operational boredom, not the changelog.
DevOps & reliability
CI/CD, evals that gate the merge, and the on-call story that ships with handover, because the team that builds it should be able to run it.
AI at the centre, engineered all the way down.
The AI is only as good as the system under it. We build both, from the model layer to the infrastructure it runs on. These are the four we're asked for most.
AI you can put a number behind.
Features that hold up when a customer checks the maths.
Most LLM features fail on accountability, not capability. We build the deterministic layer underneath: numbers computed in code before the model sees them, every claim traced to its source.
- Retrieval and grounding pipelines
- Evaluation suites that gate prompts in CI
- Cost, latency, and accuracy budgets
Ship the product you can't staff for.
Idea to production, with a team you don't have to hire.
We take it from scoping through to a running system (backend, web, mobile) and hand over something your own engineers can pick up on day one.
- Architecture and technical scoping
- Go services, Next.js web, Flutter mobile
- CI/CD, tests, and documentation
Fix the system that's buckling.
Headroom for the next order of magnitude, without a rewrite.
Something works at your current scale and won't at the next one. We find the constraint that actually binds, fix that first, and leave the rest alone until it earns the attention.
- Performance and correctness audit
- Incremental migration, no big-bang rewrite
- Handover to your team, with the on-call story
04 / Platform & infrastructureInfrastructure that stops waking people up.
Reproducible, observable, cheaper to run than today.
Cloud infrastructure built to be boring, usually untangling something that grew by accident and putting it back on rails, without a freeze on shipping.
Four rules we don't bend.
Every team says they care about quality. These are the specific calls we make when caring about it costs something.
Correctness is a feature.
A system that is fast and wrong is just wrong sooner. We pick the guarantee first (ordering, consistency, durability) and then make it fast inside that constraint.
Boring infrastructure, ambitious products.
Postgres, Go, containers, Terraform. We spend our novelty budget on the product surface, not on the parts that page someone at 3am.
AI that reasons over facts, not vibes.
Models narrate; they don't compute. Every number an AI feature reports is derived deterministically before the model ever sees it, and every claim traces back to its source.
Own the thing you ship.
The engineer who builds a service runs it. That closes the loop between the design decision and the pager, which is the only thing that reliably produces good design decisions.
From the engineering blog.
What we learned building the things we ship.
Let's build something that lasts.
Tell us what you're working on. We'll tell you honestly whether we're the right team for it.