The full stack behind every delivery, and how we choose from it
We work across models, orchestration frameworks, vector stores, cloud platforms and classical machine learning libraries. The list matters less than the selection logic, so this page covers both.
The full stack behind every delivery
We pick the right tool for your use case, not the most familiar one. Here is what we work with across all practice areas.
AWS and GCP are where most of this work lands
Those two carry the majority of our production deployments, and they are where our deepest operational experience sits. Azure is used where a client is already committed to it, and we are certified on all three so the choice is yours rather than ours.
How a tool gets picked for your project
A long stack list is easy to publish and tells you very little. What matters is the reasoning that puts one of these tools in your architecture and leaves the rest out, so here is ours.
Start from the constraint, not the tool
Latency budget, data residency, existing cloud commitment, the team who will maintain it and the regulation you answer to. Those five usually eliminate most of the options before anyone argues about frameworks.
Prefer what your team can run
A slightly slower system your engineers understand beats a marginally faster one only we can operate. If you already run Postgres and your corpus is modest, pgvector is often the right answer and a dedicated vector database is an unnecessary bill.
Keep the model layer swappable
Model quality, pricing and availability move quarterly. We route through a gateway so switching providers or mixing them by task is configuration rather than a rewrite, and so cost per request stays visible.
Measure before committing
Chunking strategy, retriever choice, reranker and model all get tested against your benchmark rather than assumed from a blog post. The evaluation harness is built early precisely so these decisions are evidence-based.
Not every problem needs a language model
Structured prediction, forecasting, fraud scoring and anomaly detection are usually better served by gradient boosting or classical time series methods. They are cheaper, faster, more explainable and far easier to defend in an audit. We recommend them where they fit, even though a generative build would be a larger engagement.
| Decision | Default we start from |
|---|---|
| Small corpus, existing Postgres | pgvector, no new database |
| Large corpus, complex filtering | Pinecone or Weaviate |
| Multi-hop reasoning over entities | Neo4j knowledge graph plus RAG Fusion |
| Deterministic multi-step process | n8n or Airflow, not an agent |
| Judgement at each step | LangGraph with approval gates |
| Strict data residency | Open-weight model inside your VPC |
| Tabular prediction | XGBoost or LightGBM |
No demos. No notebooks. Only working systems.
One partner, full accountability
Architecture through deployment through support. No vendor coordination, no accountability gaps. You have one person to call.
Production-ready from the first sprint
Every system is tested against defined success criteria before it goes live. Clean architecture, documented handoff, real monitoring from day one.
KPIs before code
We define what success looks like before writing a line. Efficiency gains, cost reductions, retrieval accuracy. We track them throughout.
You see progress every week
Working demos from sprint two, structured updates, and honest communication about blockers. No surprises at handoff.
Questions about tooling and lock-in
What clients ask before committing to an architecture.
Are you tied to a particular model provider?
No. We route through a provider-agnostic gateway and select per task. Several clients run a mix, using a frontier model for complex reasoning and a smaller or open-weight model for high-volume simple calls, which is where most of the cost saving comes from.
Can everything run on open-weight models?
Yes, where the accuracy requirement allows it. We have fine-tuned Gemma 3 27B for a legal client and run fully private deployments inside customer VPCs. Open-weight models cost more in infrastructure and engineering, and less per request, so the crossover point depends on your volume.
Which cloud do you build on?
Whichever you already use. We are certified on AWS, Azure and GCP, and we build on your existing commitment rather than moving you. Where there is no existing preference, we recommend based on your data gravity and compliance requirements.
Not sure which architecture fits your problem?
An architecture review maps your workflow and data, models the cost and gives you a phased plan you can act on, whether or not we build it.