Comprehensive AI solutions from strategy to deployment
We help teams make intelligent decisions about where and how to deploy AI.
This includes feasibility analysis, ROI modeling, architecture planning, and vendor evaluation. We assess your data readiness, identify high-impact use cases, and create implementation roadmaps that align with business goals.
For: Leadership teams planning AI initiatives, product teams exploring intelligent features, CTOs evaluating build vs. buy decisions.
Outcomes: Clear technical strategy, realistic timelines, prioritized opportunities, reduced implementation risk.
We build and fine-tune models for specific domains and tasks.
This includes training classifiers, developing retrieval systems, building recommendation engines, and creating domain-specific embeddings. We work with both open-source and commercial models, optimizing for accuracy, latency, and cost.
For: Products requiring specialized AI capabilities, teams with proprietary data, organizations needing models that outperform general-purpose solutions.
Outcomes: Models trained on your data, improved task performance, control over intellectual property, reduced dependency on external APIs.
We integrate language models into products and workflows using OpenAI, Anthropic, Google, and open-source alternatives.
This includes prompt engineering, context management, output validation, function calling, multi-model orchestration, and fallback strategies. We build systems that handle model failures gracefully and optimize for token efficiency.
For: SaaS platforms adding conversational features, internal tools automating knowledge work, products requiring text generation or analysis.
Outcomes: Reliable LLM-powered features, reduced latency and costs, robust error handling, maintainable prompt libraries.
We build autonomous systems that execute multi-step workflows with minimal human intervention.
This includes document processing pipelines, intelligent routing systems, customer support automation, data enrichment agents, and research assistants. We design agents with clear boundaries, human checkpoints, and audit trails.
For: Operations teams reducing manual work, customer success teams scaling support, research teams processing large information sets.
Outcomes: Automated workflows, reduced operational costs, faster processing times, consistent quality.
We build infrastructure that collects, processes, and prepares data for model training and inference.
This includes ETL pipelines, vector databases, embedding generation, data labeling workflows, and feature stores. We ensure data quality, handle versioning, and optimize for retrieval speed.
For: Teams training custom models, products requiring semantic search, organizations centralizing knowledge bases.
Outcomes: Clean, accessible data, faster model iteration, improved retrieval accuracy, reduced data preparation overhead.
We build custom software that augments employee capabilities using AI.
This includes intelligent dashboards, research assistants, content generation platforms, document analysis tools, and automated reporting systems. We focus on interfaces that fit existing workflows and require minimal training.
For: Enterprises automating internal processes, teams reducing repetitive knowledge work, organizations improving decision-making speed.
Outcomes: Increased productivity, better resource allocation, faster insights, reduced burnout from manual tasks.
We embed intelligence into customer-facing platforms.
This includes recommendation systems, predictive analytics, smart search, content generation, personalization engines, and conversational interfaces. We design features that differentiate products and drive user engagement.
For: SaaS founders adding AI capabilities, product teams improving retention, startups building AI-native platforms.
Outcomes: Differentiated product features, improved user metrics, new revenue opportunities, competitive positioning.
We design infrastructure that supports AI workloads at scale.
This includes selecting compute resources, architecting storage solutions, designing network topology, and planning for geographic distribution. We optimize for both performance and cost, with particular attention to GPU utilization and data transfer patterns.
For: AI products entering production, startups planning infrastructure budgets, enterprises migrating AI systems to cloud.
Outcomes: Scalable infrastructure, predictable costs, optimized resource utilization, reduced deployment friction.
We build deployment pipelines that move code and models from development to production safely.
This includes automated testing, model validation, gradual rollouts, rollback mechanisms, and deployment monitoring. We integrate model versioning and ensure reproducible builds.
For: Teams shipping AI features frequently, organizations requiring deployment reliability, products serving production traffic.
Outcomes: Faster release cycles, reduced deployment risk, automated quality checks, clear audit trails.
We manage infrastructure using declarative configurations that version control and automate provisioning.
This includes Terraform, Kubernetes manifests, Helm charts, and configuration management. We build systems that are reproducible across environments and easy to modify.
For: Teams managing complex infrastructure, organizations requiring compliance documentation, projects scaling across regions.
Outcomes: Consistent environments, faster provisioning, documented infrastructure, simplified disaster recovery.
We implement monitoring systems that track model performance, infrastructure health, and user experience.
This includes logging, metrics collection, alerting, distributed tracing, and anomaly detection. We use AI to identify patterns in system behavior and predict potential failures.
For: Production AI systems, teams managing distributed services, organizations requiring uptime guarantees.
Outcomes: Faster incident response, proactive issue detection, clear system visibility, improved reliability.
We architect systems that handle variable load and high throughput for AI inference.
This includes load balancing, autoscaling, caching strategies, batch processing, and queue management. We optimize for both real-time and asynchronous workflows.
For: Products with growing user bases, applications with spiky traffic patterns, systems processing large batches.
Outcomes: Consistent performance under load, efficient resource usage, reduced latency, controlled scaling costs.
We analyze and reduce infrastructure spending without sacrificing performance.
This includes right-sizing resources, implementing caching, optimizing model inference, using spot instances, and architecting for cost-aware scaling. We provide detailed cost breakdowns and forecasting.
For: Startups managing burn rate, enterprises controlling AI budgets, teams optimizing margins.
Outcomes: Reduced monthly costs, better cost predictability, optimized resource allocation, improved unit economics.
We build full-stack applications where AI capabilities are central to the product experience.
This includes user authentication, subscription management, API layers, admin interfaces, and AI-powered features. We design systems that scale with user growth and model complexity.
For: Founders launching AI products, teams rebuilding legacy platforms with intelligence, enterprises building internal SaaS tools.
Outcomes: Production-ready platforms, scalable architecture, modern user experiences, integrated AI features.
We create interfaces that make AI system outputs visible and actionable.
This includes analytics dashboards, model performance monitors, data labeling interfaces, and configuration panels. We design for both technical and non-technical users.
For: Operations teams managing AI systems, product teams monitoring model behavior, executives tracking AI ROI.
Outcomes: Clear system visibility, faster decision-making, simplified model management, improved operational control.
We develop web applications that leverage AI for core functionality.
This includes document analysis tools, content generation platforms, research assistants, automated workflow builders, and semantic search interfaces. We focus on speed, reliability, and intuitive UX.
For: Teams building productivity tools, enterprises automating knowledge work, startups creating AI-native experiences.
Outcomes: Functional web applications, responsive interfaces, integrated AI capabilities, production-grade reliability.
We build iOS and Android applications that embed intelligence.
This includes on-device AI, cloud-based inference, image recognition, voice processing, and personalization. We optimize for battery life, offline functionality, and app store compliance.
For: Consumer-facing AI products, enterprise mobile tools, startups launching AI-powered apps.
Outcomes: Native mobile experiences, performant AI features, smooth user interfaces, app store approval.
We design backend systems that expose AI capabilities through clean, documented APIs.
This includes RESTful APIs, GraphQL endpoints, webhook systems, and SDK development. We prioritize versioning, authentication, rate limiting, and developer experience.
For: Platforms offering AI as a service, teams building ecosystems, products requiring third-party integrations.
Outcomes: Extensible systems, clear integration paths, external developer adoption, flexible architecture.