OneSix

Turn data and AI ambition into a prioritized, executable roadmap with clear business value, architecture direction, and ROI targets.

Creates a practical governance operating model that helps teams use consistent, trustworthy data and govern AI responsibly.

Connects and structures business data in a reliable, scalable platform that gives analytics and AI a dependable foundation.

Give teams trusted, real-time visibility and self-service access to consistent metrics so they can make decisions with confidence.

Move AI beyond pilots with production systems designed to run reliably, stay accurate, and deliver measurable business value.

Keeps governance policies, data quality, access, and lineage operating continuously as data evolves.

Sustain data and AI momentum with an embedded team that continuously advises, identifies opportunities, and delivers work as priorities evolve.

Manage and deploy Snowflake agents, search services, and MCP servers through existing dbt pipelines with versioning, dependency-aware ordering, and governed access.

Move AI agents from prototypes into dependable production workflows with governed context, operational controls, and integrations tailored to business processes.

Identify at-risk customers earlier and give teams scores, context, and recommendations to support timely retention interventions.

Gives customers and teams trusted, direct answers from enterprise data, documents, and operational knowledge through natural-language interfaces in tools they already use.

Unify customer data into a trusted profile that helps teams track lifecycle stages, segment audiences, and model customer lifetime value.

Turn unstructured documents into usable data for analytics, workflows, search, and question answering.

Data & AI Strategy

OneSix assesses an organization’s data and AI environment and develops an executable roadmap tied to business outcomes. The 6–10 week engagement covers current-state assessment, use case prioritization, architecture direction, and business case development. Deliverables include a review of platforms, pipelines, capabilities, and existing use cases; a prioritized roadmap; recommendations for architecture and tooling; and ROI targets and funding materials for priority use cases.

Category: Data and Analytics Consulting

• Focus Investment On High-Value Use Cases
  Give leadership an agreed sequence of use cases based on value, feasibility, and readiness, rather than a wish list of competing initiatives.
• Align Architecture With Priority Use Cases
  Make platform and architecture decisions in response to prioritized business needs, while identifying the changes required in the current environment to support delivery.
• Build A Fundable Case For Investment
  Equip decision-makers with documented value targets and assumptions they can use to evaluate, approve, and fund priority data and AI work.
• Move Priority Work Toward Delivery
  Give teams a roadmap designed for execution, with ownership, supporting architecture decisions, and ROI targets attached to priority use cases.
• Reveal Constraints And Opportunities
  Gain a clear view of the current environment, its capability and maturity, and the gaps that may affect delivery. This helps leadership understand what to address and where the strongest opportunities lie.

Governance Blueprint

Governance Blueprint is a structured engagement that designs a continuous operating model for trustworthy data and responsible AI. Across core governance, responsible AI, compliance design, and technology direction, OneSix establishes standardized data language and stewardship, AI policies and guardrails, regulatory controls, and vendor-neutral technology recommendations. The 6–8 week engagement produces a designed operating model tailored to the organization’s environment and governance maturity, built to run beyond go-live rather than end as a one-time framework.

• Establish Governance That Continues Beyond Go-Live
  Creates an operating model designed to keep governance functioning after the engagement closes, with a stewardship structure, policy framework, shared language, and continuous operating cadence.
• Standardize Definitions And Assign Data Stewardship
  Helps teams use consistent data definitions and clarifies who owns, stewards, curates, and certifies each asset. Designed workflows and shared governance practices help bring governance out of departmental silos.
• Govern AI With Policies And Lifecycle Oversight
  Provides a designed framework for governing how AI is developed and used, with policies, guardrails, oversight, observability, drift monitoring, and fairness testing.
• Translate Regulations Into Data Controls
  Connects regulatory obligations to the organization’s data and workflows, specifying classification, lineage, policy enforcement, and access controls that support compliance.
• Select Tools That Fit Your Governance Model
  Recommends governance technology after the operating model is designed, so platform choices support the program and suit the organization’s environment, maturity, and scale.

Data Foundations

OneSix designs and deploys centralized, production-ready data platforms that connect core business systems and turn raw data into a coherent, reliable layer for analytics and AI. The engagement covers platform architecture, data integration, data modeling, and performance and cost optimization. It can build on an existing platform investment or evaluate options based on data sources, workloads, and team needs. The Snowflake Quickstart Accelerator is delivered as part of a Data Foundations engagement and establishes a production-ready Snowflake environment with configured security, access, cost controls, development conventions, and integration-ready setup. The result is a platform the team can operate and extend, with integration patterns and modeling decisions documented.

• Build A Platform For Production
  Get a centralized platform designed for the workloads it needs to support, with scalable infrastructure and DevOps tooling integrated from go-live.
• Unify Data Across Core Systems
  Bring data from business systems, point solutions, and unstructured sources into one reliable layer using ingestion and orchestration suited to the workload.
• Launch A Production-Ready Environment In A Day
  Get a configured Snowflake foundation deployed in a day rather than undertaking a multi-week initial setup.
• Enforce Scoped Access And Built-In Security
  Start with configured identity and network controls, role-based permissions, and a development-scoped setup for Cortex AI.
• Structure Data For Analytics And AI
  Give downstream analytics and AI a consistent foundation with modeled data, semantic definitions, documented lineage, and metadata.
• Assign Scoped Roles And Dedicated Service Accounts
  Provide teams and platform tools with defined roles and service accounts, with permissions prepared for common integration patterns.
• Control Platform Performance And Costs
  Keep the platform fast as it scales and make usage more predictable through workload tuning, cost monitoring, and capacity planning.
• Structure Environments For Development Through Production
  Establish environment conventions and data architecture that support development, QA where needed, and production.
• Configure Cost Controls From The Start
  Include cost controls in the initial environment setup rather than leaving them for a later phase.
• Connect Common Data And Analytics Tools
  Use prepared service-account access for common integration patterns, including default readiness for Fivetran and compatibility with listed ingestion, transformation, orchestration, and analytics tools.
• Enable Cortex Code With Scoped Permissions
  Provide Cortex Code in the development environment using a dedicated role with minimal permissions, with an opt-out where AI is not needed.
• Document The Setup For Ongoing Team Use
  Give the team documentation of the environment’s roles, conventions, structure, and configuration decisions so they can maintain and extend what was built.
• Include Git And dbt Setup
  Include the Git integration and dbt scaffold identified among the accelerator’s setup components.

Analytics

OneSix designs and deploys an analytics, business intelligence, and conversational layer that gives organizations real-time visibility into current performance and what is coming next. The engagement covers BI platform and semantic-layer design, KPI definitions and ownership, dashboards, automated reporting and alerting, embedded analytics, and natural-language data access. The dashboard and conversational layers use the same KPI framework, with accuracy guardrails and traceable answers. Typical engagements run two to four months and include user enablement to support adoption.

• Standardize Metrics Across The Organization
  Establish shared metric definitions, calculations, and ownership to reduce discrepancies and provide a consistent basis for reports, dashboards, and decisions.
• Align Analytics With Business Decisions
  Organize analytics around the questions teams need to answer and the workflows where decisions happen, using the existing data layer and tools.
• Ask Data Questions In Plain Language
  Enable users to ask ad hoc questions in natural language and receive responses grounded in governed metrics, with safeguards and traceability to support accuracy and verification.
• Surface Performance In Real Time
  Give operational teams and leaders visibility into the metrics relevant to their workflows, supporting timely decisions based on a shared KPI framework.
• Support Everyday Analytics Adoption
  Equip users to work with the analytics layer in their day-to-day decisions, supporting adoption beyond initial platform deployment.
• Deliver Timely Reports And Alerts
  Automate reporting and connect alerts to decision-driving metrics so teams can see relevant information and act without depending on manual exports.
• Bring Insights Into Existing Workflows
  Make analytics available within the systems people use to make decisions, rather than requiring them to move between separate tools.

Artificial Intelligence

OneSix engineers AI systems at scale, including predictive machine learning, computer vision, and LLM and agentic applications. Engagements are scoped around a real business problem, built for the production environment, and instrumented to make ROI visible from launch through retraining. Work can cover use-case development, model training and validation, MLOps and lifecycle management, and application integration. Systems include monitoring and other production capabilities to help maintain performance as data and conditions change.

Category: AI Development Services

• Monitor And Retrain Models Over Time
  Monitoring, drift detection, and retraining triggers help teams see performance changes and maintain models as data and conditions evolve.
• Build Models Matched To The Problem
  Model selection and architecture are driven by the problem’s statistical properties, with feature engineering and validation supporting production use.
• Deploy LLMs And Agents In Production
  Production-oriented retrieval, orchestration, context, evaluation, and monitoring components support LLM applications and agentic workflows.
• Apply Vision Models In Operating Workflows
  Models are built for the environment where inference occurs and integrated into workflows where their outputs are used.
• Integrate AI Into Production Workflows
  Integration connects AI outputs to the applications and workflows where they are used, within the production environment.
• Scope AI Around Business Problems
  Use-case development ties the technical work to a real business problem and measurable outcomes.
• Train And Validate Production Models
  Training and validation prepare models for deployment in their intended production environments.

Governance Programs

OneSix implements governance operating models as running systems, deploying and configuring governance platforms and integrating them with data sources, identity systems, and operational tools. The service automates policy-based access controls, continuously monitors data quality, and provides searchable cataloging, end-to-end lineage, and audit-ready documentation. It can also extend monitoring to AI models through drift detection and observability. Implementations typically take two to four months and are designed to operate after the engagement closes.

• Monitor Data Quality Continuously
  Applies quality rules and monitoring to important data domains, with alerts and remediation workflows to address problems before they affect downstream systems.
• Trace Data Lineage And Maintain Audit Trails
  Makes data assets searchable and their origins and transformations traceable, while continuously producing audit-ready documentation and showing the impact of upstream changes.
• Enforce Access Policies And Govern Data Requests
  Applies automated, rules-based access controls according to policy and sensitivity, and supports governed request workflows and self-service discovery of approved data assets.
• Deploy Governance Platforms Into Existing Environments
  Integrates and configures governance platforms within the organization’s data environment and existing technology stack, aligning the implementation with its operating model and policies.
• Detect Model Drift And Anomalies
  Monitors AI models for drift and anomalies and provides real-time alerts, extending the governance monitoring discipline beyond data pipelines.

Operating Partnership

A long-term, embedded relationship with a dedicated team of senior practitioners delivering strategic advisory, an ongoing innovation pipeline, and execution across OneSix’s six service lines. The team carries institutional context across initiatives, supports executive planning and reporting, and scales with changing workloads and priorities. The partnership is designed for organizations running multiple active data and AI workstreams that need continuous delivery rather than disconnected projects.

Category: Data and Analytics Consulting

• Deliver Work With A Dedicated Team
  A team of senior practitioners delivers projects across strategy, governance, data foundations, analytics, AI, and governance programs, with staffing tailored to the workload.
• Align Work With Shifting Priorities
  Executive-level planning and regular roadmap maintenance help keep data and AI work aligned with business goals as priorities change.
• Preserve Context Across Initiatives
  Work can continue with the team’s accumulated knowledge of the organization’s environment, goals, and stakeholders, rather than starting from scratch for each project.
• Prioritize New Use Cases
  A continuing pipeline of evaluated opportunities helps organizations identify and resource data and AI use cases that fit their strategy and ROI thresholds.
• Secure Prioritized Team Capacity
  Prioritized staffing and resource allocation help the organization move work forward as needs and priorities change.
• Access Preferred Engagement Pricing
  Preferred pricing is available across partnership engagements, including additional engagements outside the core scope.
• Optimize Platforms Over Time
  Ongoing work can include platform optimization as the organization’s data and AI environment evolves.

dbt Cortex Agent

The dbt Cortex Agent Accelerator is a proprietary OneSix dbt package that brings Snowflake Cortex Agents, Cortex Search services, and Snowflake-managed MCP servers into dbt workflows as models. Define them in the dbt project and deploy them through its existing CI/CD pipeline, with version control, dependency-aware deployment, and governance. Raw Snowflake specifications pass through verbatim, while dbt ref() dependencies order deployments after the semantic views and search indexes they rely on.

• Manage Cortex Services In dbt
  Treat agents, search services, and managed MCP servers as dbt models, so teams can define them in their project and run them using dbt.
• Deploy Through Existing Pipelines
  Use the dbt project’s existing deployment process to ship the Cortex stack alongside code, without introducing separate AI orchestration.
• Use Snowflake Specs As Written
  Write models using the raw Snowflake agent or MCP server YAML. Verbatim passthrough avoids wrapper limitations and works with new Snowflake features as they ship.
• Deploy Dependencies In Order
  Use dbt dependencies to deploy upstream semantic views and search indexes before the agents and MCP servers that depend on them.
• Apply Snowflake Access Controls
  Keep agent-related objects under Snowflake role-based access control, with scoped permissions and an audit trail, while data stays in Snowflake.
• Preserve Agent Change History
  Keep a named history of agent changes on each run, disable versioning for fast development iteration, or use a full refresh to start with a clean history.
• Rerun Deployments Safely
  Run deployments repeatedly without unsafe duplicate effects. Search indexes are altered in place during steady-state runs, preserving incremental refresh.
• Capture Feedback Across Agents
  Collect agent ratings, comments, and conversation context in a shared table using the provided procedure, without building custom logging.

Agentic AI

OneSix architects and deploys production agentic systems with deliberate design patterns, a governed context layer, operational integrations, and the infrastructure needed to run reliably at scale. Engagements combine agent development with evaluation, observability, cost discipline, and human escalation controls. A priority agent is validated in production within the first sixty days, establishing reusable runtime, integration patterns, and operational playbooks for subsequent builds.

Category: AI Development Services

• Deploy Agents Into Production
  Validate a priority agent in production within the first sixty days, then use the established runtime and playbooks to support subsequent builds.
• Ground Decisions In Governed Context
  Give agents business context and access rules that help make their decisions informed, auditable, and aligned with operational requirements.
• Match Architecture To Workloads
  Choose an architecture based on the workload and its failure modes, rather than inheriting an unplanned pattern that may not fit production needs.
• Connect Agents To Business Systems
  Integrate agents with data and operational tools so workflows can use relevant information and interact with the systems where work happens.
• Evaluate And Trace Agent Behavior
  Score agent performance against relevant data and reconstruct the path behind individual requests to support accountability in production.
• Escalate Decisions To People
  Define where people review or take over decisions, including escalation for low-confidence outcomes and checkpoints built into the system architecture.
• Monitor Agents In Production
  Observe agent behavior and monitor for drift after deployment, helping teams maintain visibility as systems operate over time.
• Control Agent Operating Costs
  Apply cost discipline and spend limits to agent workflows to address resource use as systems run at scale.
• Run Agents On Production Infrastructure
  Establish a containerized runtime and CI/CD infrastructure as part of deploying agents for production operation.
• Adapt Agents To Industry Workflows
  Apply agent workflows to stated industry processes, from proposal and RFP work to student services, clinical operations, and manufacturing tasks.

Churn Prediction

A Snowflake-native churn prediction system that combines machine learning scoring with Cortex AI. It analyzes structured customer data and unstructured signals from sources such as call notes and emails, weights scores by customer value, and provides context and recommendations. The system includes a conversational interface for querying scores and customer history, alerts when scores change, and periodic retraining using new churn outcomes and account executive feedback.

• Identify And Prioritize At-Risk Customers
  Combine structured and unstructured customer signals to score churn risk, with scores weighted by customer value to help teams prioritize attention.
• Understand Risk And Guide Intervention
  Give teams context about the drivers and triggers behind churn risk, along with recommendations that support timely intervention.
• Query Scores And Customer History
  Let users access churn scores, customer history, and aggregate rollups through natural-language queries, with options to extend the interface to Slack, Teams, and existing BI layers.
• Alert Teams When Scores Change
  Surface changes in customer risk scores through an alert pipeline so teams can respond to updated churn signals.
• Refresh Models With New Outcomes
  Use new churn outcomes and account executive feedback in periodic retraining to keep the model updated as new information becomes available.
• Connect Common Customer Data Sources
  Use reusable pipeline patterns for common CRM, marketing, support, and commerce data sources.

Conversational AI

OneSix builds production conversational AI systems that put enterprise data, documents, and operational knowledge behind natural-language interfaces. The systems combine semantic modeling for structured data, retrieval-augmented generation for unstructured content, and integrations with tools users already use. Governance frameworks, verified query patterns, and ambiguity guardrails support accurate, appropriately scoped responses. The Conversational AI Accelerator offers a Snowflake-native query layer using Cortex Analyst, Cortex Search, and Snowflake Intelligence, with role-based access, configurable cost controls, and support for multiple data domains.

• Ask Questions In Plain Language
  Users can ask questions directly instead of relying on SQL, navigating portals, or waiting for analysts and subject-matter experts.
• Clarify Ambiguous Questions And Ground Answers
  The system uses governed data definitions and source-grounded retrieval, and can request clarification when a question is ambiguous.
• Query Structured Data With Natural Language
  Users can ask questions about business data and metrics without having to know the underlying table schemas or query language.
• Find Answers In Documents And Content
  Users can retrieve information from documents and other unstructured content through the conversational interface.
• Verify Answers Against Their Sources
  Users can inspect the data, model fields, SQL, documents, or passages behind a response to check it against its underlying source.
• Use Conversational AI In Existing Tools
  Teams can access conversational interfaces in tools and applications they already use, and eligible Tableau and Power BI semantic models can be converted rather than rebuilt from scratch.
• Respect User Permissions And Data Boundaries
  Responses can be scoped according to user permissions and data boundaries; the Snowflake accelerator inherits configured role-based access.
• Expand Across Teams And Data Domains
  Organizations can start with a single team and extend the accelerator to additional semantic models and data domains without rearchitecting.
• Control Cortex Credits And Query Costs
  Configurable limits help organizations govern Cortex credit consumption and query costs within the Snowflake accelerator.

Customer 360

OneSix builds production-ready Customer 360 platforms by integrating data from existing systems into a unified customer profile. The solution combines identity resolution, lifecycle tracking, segmentation logic, and customer lifetime value modeling, with an analytics layer and user enablement. A Customer 360 Accelerator offers pre-built data models, integration templates, delivery frameworks, data quality rules, and industry schemas to reduce time to value.

• Unify Customer Data Across Systems
  Bring customer information together from existing systems into a single profile that teams can use as a trusted basis for their work.
• Connect Records To The Same Customer
  Resolve customer identities across source systems so teams can work from unified profiles rather than disconnected records.
• Track Customers Across Lifecycle Stages
  Make customer activity and lifecycle stages visible across systems to support lifecycle analysis and management.
• Build More Informed Customer Segments
  Use unified customer data and segmentation logic to support audience building and campaign activation.
• Model Customer Lifetime Value
  Build a more complete basis for modeling customer lifetime value across customers, cohorts, and channels.
• Put Customer Insights Into Use
  Deploy an analytics layer and provide user enablement so teams can act on the platform’s unified customer data.
• Accelerate Customer 360 Deployment
  Use pre-built data models, reusable integration patterns, delivery frameworks, and data quality rules to reduce time to value compared with a custom build.
• Apply Schemas Built For Your Industry
  Choose purpose-built Customer 360 schema variants for Marketing, Higher Education, or Healthcare & Life Sciences.

Document Intelligence

OneSix builds Document Intelligence systems that turn unstructured documents into structured, analytics-ready data. Solutions are designed around specific document types, extraction requirements, and downstream use cases. The Snowflake-native accelerator uses Document AI and Cortex to ingest and extract information, with schemas defined for each document type. Confidence scores and a review application help route uncertain extractions for human review, while governed Snowflake tables make the output available to BI tools and downstream workflows. The extracted corpus can also support semantic search, retrieval-augmented generation, and question answering.

• Extract Information From Documents
  Process documents that traditional data systems cannot readily query or analyze, including scanned and image-based files. The extraction pipeline is designed to handle varied layouts and content.
• Review Uncertain Extractions
  Route low-confidence fields for human review instead of allowing uncertain results to pass through unchecked. The review app displays source documents alongside extracted data, and reviewer decisions feed back into the pipeline.
• Adapt Extraction To Document Types
  Define the information to extract for each document type and update schemas as requirements or document types change, without rebuilding a separate template for every format.
• Prepare Extracted Data For Analytics
  Make structured document data available in governed tables for BI tools, reporting, and downstream analytical workflows.
• Search Documents And Answer Questions
  Use the same parsed corpus for semantic search, retrieval-augmented generation, and question answering over document content.
• Tailor Extraction To Document Requirements
  Align extraction architecture, schemas, and model configuration with the documents and information requirements relevant to the intended use case.
• Validate Extraction Accuracy
  Measure extraction performance against representative documents and relevant use-case metrics, then apply quality assurance and governance practices to monitor accuracy over time.
• Route Data Into Existing Workflows
  Connect extracted data with the workflows, systems, and people that use it, and support document processing at scale.