Solutions
AI-Powered Products
AI product agency for US teams building copilots, automations, and data-grounded features—safety-first by design.
Who It's For
Perfect for
Products needing intelligent automation
Platforms requiring natural language understanding
Applications with predictive analytics needs
Businesses wanting to leverage AI for competitive advantage
Products requiring personalization at scale
Use Cases
Built for these scenarios
Deliverables
Everything you receive
Timeline
Typical timeline
Discovery
AI use case definition, model selection, and architecture planning
Build
AI integration, model fine-tuning, inference pipelines, and testing
Launch & Stabilize
Performance optimization, cost monitoring, and production deployment
Measurement
What we measure
These are measurement categories, not promised outcomes. Baselines, targets, and test conditions are agreed from your requirements and operating data during discovery.
Quality: Task-specific evaluation set and acceptance threshold
Latency: End-to-end response time measured by workflow
Cost: Per-task usage and caching effectiveness tracked
Scale readiness: Representative concurrency and rate tests
Reliability: Failure, fallback, and provider-outage paths measured
Considerations
Risks & assumptions
AI model performance may require iteration
API costs can scale with usage
Hallucinations in LLMs need mitigation
Regulatory compliance for AI varies by region
Related
You might also need
AI Capability Layer
Add AI to this solution
Common AI modules teams add to accelerate support, ops, and internal workflows—without rebuilding the core product.
Start with the buyer sprint
The $3k–$5k opportunity sprint defines the right first workflow, measurable success criteria, risk controls, and rollout decision.
FAQ
Frequently asked questions
We use OpenAI GPT-4, Claude, and other leading models based on your needs. For custom requirements, we can fine-tune models or use open-source alternatives. We choose models based on accuracy, cost, and latency requirements.
We use Retrieval-Augmented Generation (RAG) to ground responses in your data, implement prompt engineering, add fact-checking layers, and provide source citations. We also set confidence thresholds.
Yes. For specialized use cases, we can fine-tune existing models or train custom models. However, most use cases work well with pre-trained models and fine-tuning, which is faster and more cost-effective.
We implement caching, batch processing, model selection based on use case, and usage monitoring. We optimize prompts to reduce token usage and choose cost-effective models when appropriate.
We can add task-appropriate bias checks, evaluation sets, disclosure, logging, and human review. The exact controls and limitations are documented for the use case rather than presenting fairness or explainability as guaranteed.
Regional
Delivery considerations for your region
Data and risk discovery (United States)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (United States)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (United States)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (United States)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
Want to scope this properly?
Tell us the workflow + data sources and we’ll propose an AI roadmap with guardrails and clear next steps. USD-based engagements.
Scoped around your requirements. No-pressure consultation.