01 / Build
AI Research & Engineering
Resolve technical uncertainty, develop custom models and systems, and take validated work into production.
Explore research & engineering ↗Applied AIBuild + Assure
Applied AI for problems that don’t have off-the-shelf solutions.
We build custom AI systems and independently assess the systems, claims, and behaviors behind consequential decisions. Senior-led research, engineering, and assurance for enterprise teams.
TSG / System path01–04
- 01DefineProblem · constraints · economics
- 02De-riskExperiments · evidence · evaluation
- 03Build or assessSystems · claims · behavior
- 04DeliverProduction system · decision evidence
Typical engagements begin at $50,000.
- Model
- Senior-led
- Scope
- Build + Assure
- Base
- Montréal · Remote
Two practices. One evidence standard.
02 / Assure
AI Assurance
Independently examine AI systems, technical claims, and behavior before a consequential purchase, investment, or deployment.
Explore AI assurance ↗Assure / AI Assurance
Evidence for consequential AI decisions.
Three services for buyers, investors, technical leaders, and teams whose systems or claims need to withstand scrutiny.
Technical Due Diligence & AI Claim Verification
Before you buy, fund, or rely on an AI system, we independently test what it can actually do.
Examine serviceTechnical Challenge & Defense
We find the questions your presentation cannot withstand, then help you fix the answers.
Examine serviceAdversarial Interaction & Behavior Evaluation
We test whether your AI remains reliable when a user is persuasive, misleading, persistent, or confidently wrong.
Examine serviceTechnology ecosystems
Built for enterprise platforms.
Google Cloud
Microsoft
AWS
SAP Business Network
OpenAI Partner Network
C3 AI Partner Program
Salesforce Partner CommunitySelected prior client experience
Prior consultancy for globally recognized organizations.
Where we enter
When the problem goes beyond an AI demo.
The difficult part is rarely accessing a model. It is determining what will work under real constraints and making it reliable enough to operate.
01
An AI initiative carries significant technical uncertainty.
02
A promising prototype needs to become a production system.
03
Off-the-shelf products cannot satisfy the technical requirements.
04
Proprietary data or workflows demand a custom approach.
05
Quality, latency, cost, reliability, or evaluation is the bottleneck.
06
An AI purchase or investment depends on claims that need independent verification.
07
A technical narrative must withstand enterprise or investor scrutiny.
08
An assistant must remain reliable under misleading or persistent user pressure.
Capabilities
Research depth. Production discipline.
We work across the AI system, from experiments and models to evaluation, infrastructure, integration, and operations.
Applied AI R&D
Resolve technical uncertainty before committing to a large implementation.
Examine capabilityCustom ML & Model Development
Build around the problem when a generic product cannot meet the requirement.
Examine capabilityGenerative & Multimodal AI
Engineer systems that work across text, image, video, audio, and structured data.
Examine capabilityComputer Vision
Turn visual data into reliable decisions, measurements, and production workflows.
Examine capabilityAgentic & Knowledge Systems
Move beyond isolated prompts toward systems that reason, retrieve, and use tools.
Examine capabilityEvaluation & Optimization
Know whether the system works and where quality, cost, or reliability breaks down.
Examine capabilityAI Infrastructure & Deployment
Give models the platform, data, observability, and controls needed for production.
Examine capabilityPrototype → Production
Turn research code and internal demos into maintainable operational systems.
Examine capabilityHow to engage
Built around decisions and operating outcomes.
Build through a research sprint, production system, or enterprise program. Commission assurance around a defined purchase, investment, presentation, or deployment decision.
Applied AI R&D Sprint
For an important AI opportunity where feasibility, approach, data readiness, or production risk is still unclear.
Enough technical evidence to make an informed build, buy, redesign, or stop decision.View engagementProduction AI System
For a validated AI initiative that now needs end-to-end implementation, integration, evaluation, and deployment.
A working AI system designed for real operational use, with documentation and a defined ownership path.View engagementEnterprise AI Program
For initiatives spanning multiple systems, workflows, integrations, teams, or phases of technical delivery.
AI capability integrated into the organization rather than another isolated experiment.View engagementOperating method
Evidence before scale.
Every phase reduces a specific form of technical, operational, or investment risk.
- 01
Define
Business objective, constraints, data, economics, and success criteria.
- 02
De-risk
Test the assumptions most likely to determine success or failure.
- 03
Engineer
Build the system around evidence and explicit operating requirements.
- 04
Validate
Measure quality, failure modes, cost, latency, and reliability.
- 05
Deploy
Integrate the system into production infrastructure and workflows.
- 06
Transfer or operate
Document and hand over, or continue under a defined optimization scope.
For consulting partners
Specialist AI capability inside your client engagement.
Tensor State Group can own a defined technical workstream while operating within the prime contractor’s delivery, governance, and client-relationship model.
Explore delivery partnershipsResearch & insights
Research informs engineering.
Technical notes for leaders and teams responsible for making AI systems work outside the laboratory.
01Evaluation systems
Evaluating agentic systems under real-world failure conditions
Why task completion is only one part of the evaluation problem, and how permissions, recovery, observability, and operator trust change the benchmark.
Read the research agenda02Architecture decisions
When retrieval-augmented generation stops being the right architecture
A practical decision frame for retrieval quality, knowledge volatility, latency, workflow state, and when a different system design is warranted.
Read the research agenda03Production economics
The engineering economics of model choice
How to compare frontier APIs, smaller hosted models, and specialized systems across quality, throughput, operational load, and change risk.
Read the research agenda