We don't hand you API integrations and call it AI development. We build domain-specific AI systems — fine-tuned on your data, evaluated against your standards, and ready for production from day one. Systems that deliver measurable productivity gains, not just technical outputs.
Fast
Time from kickoff to first production deployment
High
Target uptime goal on systems we deploy and operate
Client-owned
Code and model IP ownership transferred to client
Technical Capabilities
Domain-specific language models trained on your proprietary data. A well-tuned 7B model often outperforms GPT-4 on domain tasks at a fraction of the cost.
Autonomous multi-step agents that reason, plan, and execute across your tools, APIs, databases, and knowledge bases with built-in reliability and rollback.
Knowledge-grounded AI over your document corpus, wikis, codebases, and databases — with access control, hybrid retrieval, and citation tracking.
Connecting foundation models to your existing ERP, CRM, data warehouse, and business systems via secure, evaluated API layers with rate control and fallbacks.
Full-stack applications where AI is a core capability, not a feature. Designed for enterprise scale, compliance, and operational reliability from the start.
The measurement backbone for any AI system: golden datasets, automated evaluation suites, regression detection, production monitoring, and drift alerting.
Engineering Philosophy
We do not build prototypes dressed as products. Every system starts with the production architecture, evaluation framework, and monitoring design in place. Demos are deliverables — not milestones.
Generic AI performs generically. We build for your domain, your data, and your specific quality bar. Fine-tuning a 7B model on your corpus consistently beats calling GPT-4 with a generic prompt.
We write evaluation suites before we write production code. Every model, prompt chain, and agent is tested against your golden dataset and passes defined quality gates before any user sees it.
Engineering Process
Each stage has defined exit criteria. We do not advance — and you do not pay — until quality gates are passed.
System requirements deep-dive. Data audit and quality assessment. Integration mapping with existing systems. Constraint analysis: latency, cost, compliance, and privacy.
Rapid prototype of the core AI component. Baseline measurement on representative data. Feasibility validation and go/no-go decision with data to support it.
Full system architecture design. Model selection and fine-tuning strategy. Evaluation framework design. Infrastructure, security, and integration architecture.
Model training and fine-tuning. Agent orchestration and workflow implementation. API development and system integration. Iterative evaluation throughout.
Comprehensive evaluation suite execution. Red-teaming and adversarial testing. Load testing and infrastructure stress testing. Security review and penetration testing.
Staged rollout with monitoring from day one. Production alerting, drift detection, and rollback capability. Full handover with documentation and runbooks.
Technology Stack
We are model-agnostic, cloud-agnostic, and framework-agnostic. We select technology based on your requirements — cost, latency, compliance, and capability.
Foundation Models
Fine-Tuning
Agent Orchestration
Vector & Storage
Infrastructure
Evaluation & Monitoring
Stakeholder Value
CTO / Engineering
CDO / Data Teams
Business Unit Leaders
Security / Compliance
Production Readiness
Every system ships with a golden dataset evaluation suite. Minimum coverage defined at kickoff, measured at delivery.
Adversarial inputs and edge cases systematically tested before go-live. Attack patterns documented and mitigated.
Infrastructure stress tested at 3x expected peak load. Latency SLAs defined and verified under load.
Prompt injection, data exfiltration, and access control surfaces reviewed. Security findings remediated before release.
Production monitoring, drift alerting, and anomaly detection active from the first production request.
Every deployment includes a tested rollback plan. Model and system rollbacks validated in staging before production.
API integration takes days. Production AI systems take months. The difference is evaluation infrastructure, domain-specific fine-tuning, agent reliability engineering, integration hardening, and ongoing monitoring. We deliver the system you would build if you had a world-class ML engineering team — without the 18-month hiring process.
Yes. Full IP ownership is transferred at delivery. This includes the production code, fine-tuned model weights, training and evaluation scripts, documentation, and runbooks. You own everything — no licensing fees, no dependencies on supercodes to run your system.
Integration is where we excel. We have deep engineering experience connecting AI systems to SAP, Salesforce, Oracle, Microsoft 365, proprietary data warehouses, and custom internal platforms. We design for your architecture, not around it.
Fixed-scope project delivery for well-defined systems, and time-and-materials for exploratory or rapidly evolving builds. We also offer a build-operate-transfer model where we run the system in production while training your team to take ownership.
In 30 minutes we can assess your use case, estimate complexity, and give you an honest view of timeline, cost, and the productivity impact you can expect on delivery. No commitment required.
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