The systems we build, and the platforms we build them on.
We assemble production-grade AI from the best available models and tools — then deploy it where it makes sense for your data and budget. Here's what we build, and what each is good for.
AI that takes action, not just answers.
An agent doesn't just respond to a prompt — it works toward a goal. It plans, calls your tools, retrieves what it needs, and carries multi-step tasks through to completion, with human checkpoints wherever the stakes call for them.
- Service & support triage — read, classify, draft, and resolve routine requests; escalate the rest with full context.
- Research & analysis — gather from your sources, synthesize, and produce decision-ready output.
- Operations — multi-step workflows across your CRM, ticketing, and internal systems.
- Human-in-the-loop by design — agents propose; your people approve where it counts.
We connect AI to the data you can't send to the cloud.
Generic AI knows the internet. Your business runs on your documents, your policies, your records. We bridge the two — securely — so the AI answers from your knowledge and shows its sources.
- Retrieval-grounded answers (RAG) — responses drawn from your content, with citations, so they're verifiable.
- Connect to what you have — document stores, databases, wikis, ticketing, file shares, and internal apps.
- Permissions respected — users only ever see what they're already allowed to see.
- The antidote to hallucination — grounding in real data is how AI becomes accurate enough to trust.
Self-hosted, third-party, or hybrid.
The same system can run in very different places. We help you choose based on the data involved, the volume, and the budget — and we're honest about the trade-offs.
Self-hosted models
Open-weight models — running on your own servers or private cloud. Nothing leaves your environment, which makes this the strongest option for regulated, confidential, or residency-bound data. At high, steady volume the economics also turn in your favor, since there are no per-token fees.
Trade-off we'll name: higher upfront infrastructure and maintenance, and slightly slower access to the newest frontier capabilities.
Healthcare, finance, legal, and government. Strict compliance or data-residency rules. High, predictable usage. Latency-sensitive, real-time workloads.
Third-party platforms
The frontier models from OpenAI, Anthropic, and Google, accessed by API. This is the fastest path to launch and the most capable per dollar at low-to-moderate volume — with no hardware to buy and elastic scale up or down. We configure enterprise terms so your data isn't used for training.
Trade-off we'll name: data leaves your perimeter, and per-token costs grow with usage. We keep you model-agnostic to avoid lock-in.
Prototyping and early use cases. Variable or unpredictable load. Teams that need top-tier capability without an operations burden.
Hybrid deployments
You don't have to choose. Keep sensitive data on private, self-hosted models, and route everything else to cloud APIs for scale and frontier capability. We design the routing by data sensitivity, region, latency, and cost — per workload, not per company.
Why most mid-to-large orgs land here: different teams and data have different needs. Hybrid lets each get the right answer.
Organizations with a mix of sensitive and non-sensitive workloads who want control where it matters and capability everywhere else.
Which system fits your problem?
Take the two-minute assessment and we'll point you to the right system and deployment model — or skip ahead and talk to us directly.