Move AI from promising demo to trusted workflow.
Retrieval-based assistants, document intelligence, predictive models and governed agents embedded into the systems and decisions your teams already use.
What We Actually Build
Applied AI with a defined scope, a measurable outcome, and a plan for what happens when the model is wrong.
Retrieval-based assistants
Internal and customer-facing assistants grounded in your own documents, policies, and product data using retrieval-augmented generation — with citations, so answers can be checked. Deployed on the web, in Slack or Teams, or inside your service console.
Document intelligence
Extraction, classification, and validation for contracts, invoices, claims, and onboarding paperwork, with confidence thresholds that route uncertain cases to a person instead of guessing.
Predictive models
Churn, propensity, lead scoring, demand, and next-best-action models built on your warehouse data, evaluated against a baseline, and deployed where the decision is actually made.
AI in your martech and CRM
Making the AI you already pay for work: Adobe's AI Assistant, Agent Orchestrator, and Brand Concierge; Salesforce Agentforce 360, Agentforce Builder, and Agent Script grounded in Data 360. Including the data and governance groundwork these depend on.
Agentic workflows
Multi-step automations where a model plans and calls tools — with deterministic guardrails, permissions, human approval at the consequential steps, and full logging of what the agent did and why.
Analytics and reporting automation
Natural-language querying over governed semantic models, automated narrative summaries of dashboards, and anomaly alerts that arrive with context instead of a bare threshold breach.
Content operations
Drafting, variant generation, translation, metadata tagging, and brand-guideline checks integrated into your CMS or DAM workflow, with human review as a required step rather than an afterthought.
Platform and integration engineering
Model routing across providers, vector and hybrid search infrastructure, evaluation harnesses, cost and latency monitoring, prompt and version management, and deployment on AWS or Azure inside your own boundary.
Advisory and enablement
Use-case discovery and prioritisation, build-versus-buy assessments, acceptable-use and data-handling policy, vendor evaluation, and hands-on training so your team can maintain what gets built.
How We Scope an AI Engagement
Short, evidence-driven steps — so you find out early whether an idea is worth building.
1. Frame the decision
We start from the decision or task being improved, who performs it today, how often, and what a wrong answer costs. Use cases that cannot answer these questions do not get built.
2. Check the data
We look at whether the content, labels, or history required actually exist and are accessible. This is where most AI ideas either become feasible or get replaced with a better one.
3. Define success and guardrails
An evaluation set, accuracy and latency targets, escalation paths, data-handling rules, and retention terms — written down before build, and used to decide whether to proceed.
4. Prototype
A working prototype in weeks, not quarters, tested against the evaluation set with real examples and real edge cases rather than a curated demo script.
5. Productionise
Access control, monitoring, cost controls, logging, regression tests, and a rollback path. Model and prompt changes get versioned and reviewed like any other release.
6. Measure and iterate
Ongoing evaluation against live traffic, with a documented process for retraining, re-grounding, or switching models as providers release new versions.
Responsible AI, in Concrete Terms
Your data stays yours
We use enterprise model endpoints with no training on your data, document exactly which data leaves your environment, and can keep processing inside your own AWS or Azure tenancy where policy requires it.
Answers are traceable
Assistants cite the sources they drew from and log the retrieved context, so an incorrect answer can be diagnosed rather than argued about.
People stay in the loop
Consequential actions require human approval by design. Automation levels are an explicit decision recorded in the solution design, not a default.
Limits are stated
We publish known failure modes for what we build, and we will tell you when a use case is not a good fit for current models — before you spend a budget on it.
Ready to Leverage AI for Your Business?
Let's explore how AI can transform your operations and drive innovation.
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