What we do

Three practice areas. We don't work outside them.

Data foundations, agentic enablement, and Claude & AI integration — each one exists because the other two depend on it. You can't build durable agents on an ungoverned data layer, and you can't govern data without a reason a business actually needs it fixed.

Data foundationsFragmented sources resolved into a governed operating substrate

01 / Data

Data foundations

The practice area everything else depends on.

The problem. Data Cloud (Data 360) and adjacent platforms like Snowflake promise a unified customer data layer — but most orgs get there with duplicate models, unclear zero-copy boundaries, and pipelines nobody fully owns. Layer agentic AI on top of that and every failure mode compounds.

Where it's urgent right now. Salesforce moved Heroku to a sustaining-engineering model in February 2026 and closed new enterprise contracts. Existing workloads aren't broken today, but teams building new architecture on Heroku are building on a platform Salesforce has stopped investing in. That's a live migration-planning conversation, not a hypothetical one.

Our approach.

Map the current data model against what agentic use cases actually require — not what the platform vendor recommends by default
Resolve Data 360 / Snowflake boundary decisions before they get baked into architecture
Build a Heroku migration plan sized to your actual exposure, not a generic replatforming project
Leave you with a data foundation an agent can be trusted to act on

Where to start

Get a data foundation readiness score →

Legacy and point-to-point data sources are retired into one governed foundation, feeding a durable agent substrate

Every agent action is evaluated before it ships, with a governed escalation path for human review

02 / Agentic

Agentic enablement

Agentforce and agentic architecture, evaluated before it ships.

The problem. Most agent pilots demo well and stall in production because nobody defined what "working" means before build started — no evaluation harness, no failure-mode inventory, no owner for the outcome once it's live.

Where we focus. Financial Services, Health, Service, and core Salesforce — four environments where agentic decisions carry real consequence, real compliance weight, and real institutional data most vendors haven't seen before.

Our approach.

Define what "done" and "safe" mean for the agent before any flow gets built
Build against an evaluation framework, not a demo script
Design for the failure modes specific to the vertical — a Financial Services agent and a Health agent fail differently
Hand off with a monitoring and iteration plan, not a one-time deployment

Where to start

Get an agentic readiness score →

Salesforce connects to Claude through a governed bridge — data never leaves the governed environment

03 / Claude

Claude & AI integration

Anthropic implementation work alongside the native Salesforce AI stack.

The problem. Native platform AI covers a lot of ground, but not every enterprise reasoning task fits inside it — complex judgment calls, document-heavy workflows, and cases where model choice and evaluation rigor matter more than platform convenience.

Where it fits. Alongside Agentforce, not instead of it — Claude and Anthropic tooling brought in where the reasoning task calls for it, integrated into the same data foundation and governance model as everything else we build.

Our approach.

Identify where a general-purpose reasoning model outperforms a narrower platform-native agent
Integrate Claude into existing Salesforce and data workflows, not a parallel system
Apply the same evaluation discipline used across every other practice area

Where to start

Get an AI integration readiness score →

Three practice areas. We don't work outside them.

Data foundations

Agentic enablement

Claude & AI integration

Not sure which practice area you need?