01
Custom LLM Development
Purpose-built language models trained on your data
Off-the-shelf models are a starting point, not a destination. We fine-tune and train proprietary large language models on your organizational data, producing systems that understand your domain vocabulary, regulatory context, and operational nuances with precision that general-purpose models cannot match.
What we deliver
- Domain-specific fine-tuning on proprietary corpora
- Retrieval-augmented generation (RAG) architecture design
- Multi-modal model development (text, image, structured data)
- Model distillation for latency-sensitive deployments
- Continuous learning pipelines with human-in-the-loop feedback
Client Outcomes
40%
Reduction in manual document review time
3x
Improvement in domain-specific accuracy vs. GPT-4
< 200ms
Average inference latency at production scale
02
Data Pipeline Engineering
The foundational layer for accurate machine intelligence
AI is only as good as the data that feeds it. We design and build robust, real-time data ingestion and transformation pipelines that unify disparate sources into clean, governed datasets ready for model training and inference. Our architectures are built for scale, resilience, and auditability from day one.
What we deliver
- Real-time streaming ingestion (Kafka, Flink, Spark Structured Streaming)
- Data lakehouse architecture (Delta Lake, Iceberg, Hudi)
- Automated data quality monitoring and anomaly detection
- Schema evolution and backward-compatible data contracts
- Cross-cloud and hybrid data fabric design
Client Outcomes
99.97%
Pipeline uptime across production workloads
10TB+
Daily throughput for enterprise clients
60%
Reduction in data preparation time
03
AI Strategy Consulting
From boardroom vision to executable roadmap
Before writing a single line of code, we work with your leadership team to identify the highest-impact opportunities for AI across your organization. Our strategists combine deep technical fluency with business acumen to produce actionable roadmaps, governance frameworks, and investment cases that align AI initiatives with enterprise objectives.
What we deliver
- AI readiness assessments and maturity benchmarking
- Use-case prioritization using impact-effort analysis
- Total cost of ownership modeling and ROI forecasting
- Organizational change management and AI literacy programs
- Vendor evaluation and build-vs-buy decision frameworks
Client Outcomes
85%
Of identified use cases reach production within 12 months
$12M
Average annualized value unlocked per engagement
4.2x
Average return on AI investment within 18 months
04
Security & Governance
Responsible AI with enterprise-grade compliance
Deploying AI in regulated industries demands rigorous governance. We implement comprehensive security frameworks that address data sovereignty, model explainability, bias mitigation, and regulatory compliance. Every model we deploy includes a full audit trail, from training data provenance to inference decision logs.
What we deliver
- AI risk assessment aligned with NIST AI RMF and EU AI Act
- Model explainability and interpretability tooling (SHAP, LIME, attention analysis)
- Bias detection, measurement, and mitigation pipelines
- Data sovereignty controls for multi-jurisdiction deployments
- Air-gapped and on-premise deployment for classified environments
Client Outcomes
100%
Audit pass rate across SOC2 and ISO 27001 reviews
Zero
Data sovereignty violations across all deployments
< 48hr
Mean time to produce compliance documentation
05
System Integration
Embedding intelligence into your existing stack
AI creates value only when it reaches the people and processes that need it. We specialize in integrating intelligent capabilities into your existing enterprise systems — ERP, CRM, ITSM, and custom platforms — through well-architected APIs, event-driven patterns, and user-experience layers that drive adoption without disrupting operations.
What we deliver
- Enterprise API design and gateway architecture (REST, GraphQL, gRPC)
- Event-driven integration with existing middleware and ESBs
- Conversational AI embedded in Slack, Teams, Salesforce, and ServiceNow
- Custom UI/UX for AI-augmented workflows and decision support
- Legacy system modernization with AI-first architecture patterns
Client Outcomes
92%
End-user adoption rate within first 90 days
35%
Reduction in average process cycle time
< 4 wks
Time to first integrated prototype
06
Scalable MLOps
From experiment to production, automated and governed
Getting a model into production is only the beginning. We build end-to-end MLOps platforms that automate training, testing, deployment, monitoring, and retraining of your machine learning models. Our platforms ensure that models remain accurate, performant, and compliant as data distributions shift and business requirements evolve.
What we deliver
- CI/CD pipelines for ML (feature stores, model registries, A/B testing)
- Automated drift detection and model retraining triggers
- Multi-environment promotion (dev → staging → production) with approval gates
- GPU cluster orchestration and cost optimization (Kubernetes, Ray, Slurm)
- Observability dashboards for model performance, latency, and fairness metrics
Client Outcomes
10x
Faster model deployment cycle (weeks → days)
70%
Reduction in infrastructure cost through auto-scaling
99.9%
Model serving uptime SLA