AI solutions

AI is an integration problem.

Models are the easy part. The hard part is connecting them to the systems, policies and people that already run your business, safely and auditably, in a way your team can maintain after we leave. That is the work we have always done.

Where the model meets the enterprise.

Five practices, one architecture. We build with the systems you already have, not around them.

Agent systems Multi-agent workflows that do real operational work: reconciliation, monitoring, drafting, with a human decision point where it matters. Multi-agent · Orchestration · Tool use
AI integration Connecting models to the systems you already run: ERP, CRM, core banking, document stores, and the policies that govern them. SOA · BPM · Event-driven · APIs
Private deployment Cloud-neutral architecture with your data staying where your regulator expects it, and no training rights granted to anyone. AWS · Azure · GCP · On-prem
Responsible AI Evaluation, guardrails and audit trails, so an agent decision can be explained months later to someone who was not there. Evals · Guardrails · Audit · Governance
Future of AI We build so the model underneath is a configuration choice, not a rewrite. Frontier, open-weight and on-device models sit behind one interface. On-device · A2A protocols · Ambient · Model-portable

Reference architecture

One shape, whatever the domain.

Agents never touch systems of record directly. Everything passes through the fabric.

Horizon

What we are building toward.

NowSovereign deployment
Regulated clients running the whole stack inside their own boundary, with no egress to a vendor control plane.
Near termModel portability
Routing each task to the right model: frontier models for reasoning, small local models for anything that should never leave the building.
EmergingAgent to agent
Your agents negotiating directly with a counterparty agent, under policy both sides can audit.
EmergingAmbient interfaces
Voice and wearable surfaces where the interface disappears and the decision does not.

Approach

We listen first. Then we build something you can maintain.

1ListenWe start with the process, not the model. Often the answer involves less AI than expected, and we will say so.

2ArchitectA design that fits the systems you have, the policies you are held to, and the team who maintains it after we leave.

3DeliverWorking software in production, not a pilot that stalls at the security review.

4Hand overDocumentation, runbooks, and the training to run it without us. Partnership, not dependency.

Capability index

What we know how to build.

Generative AI
Retrieval, summarization, and drafting against your own corpus, with citations back to the source.
Multi-agent systems
Specialist agents that divide real operational work, with a human decision point where it matters.
Cloud architecture
Cloud-neutral by design. Your data stays where your regulator expects it.
Enterprise AI
Delivery against the systems you already run: ERP, CRM, core banking, document stores.
Mobile AI
On-device and hybrid inference for native iOS and Android surfaces.
Responsible AI
Evaluation, guardrails, and audit trails, so a decision can be explained months later.
Voice AI
Speech interfaces where hands and eyes are busy, held to the same governance as text.
Large language models
Frontier, open-weight, and on-device models behind one interface, chosen per task.
Prompt engineering
Versioned, evaluated, and regression-tested like any other production asset.
Security
Least privilege, tenant isolation, encryption in transit and at rest, and no training rights granted.

Where this applies

The architecture on this page is general. Two pages make it specific: AI integration for financial services sets out the four constraints that decide the design in a regulated institution, and Boston and Massachusetts answers the entity, location, and contracting questions. The engagement model covers how a project actually runs.

The arguments behind the architecture

Each of the practices above rests on a position we have had to defend, so the reasoning is written up rather than asserted:

What model portability actually requires, on why an abstraction layer alone does not deliver it.
What a defensible AI agent audit trail looks like, on the difference between logging and evidence.
Hosted, private endpoint, or self-hosted, on the two axes that get conflated and why open weights are not a privacy property.
Why AI pilots stall at the security review, on the eight questions that decide it.

Bring us the hard one.

The integration nobody wants to own, or the pilot that never reached production.