Generative AI Development Company

We start a generative AI strategy and readiness consulting engagement with use case prioritization—identifying which business problems have the data, model, and ROI profile to succeed in production. We assess your build vs. integrate decision: when to fine-tune open-source models, use hosted APIs, or build from scratch. As a top AI consulting company, we assess data readiness, scope compliance, and model total cost of ownership—prompt costs, inference compute, and retraining cadence. We scope compliance implications (GDPR, HIPAA, SOC 2) on deployment architecture and model total cost of ownership—prompt costs, inference compute, and retraining cadence. Clear scope and milestones prevent hidden costs and technical debt.

We develop production-grade LLM applications and custom models across the full stack: foundation model selection (open-source Llama 3, Mistral, or proprietary GPT-4o, Claude 3.7, Gemini), parameter-efficient fine-tuning (LoRA, QLoRA) on your proprietary data, and RAG system architecture covering retriever design, chunking strategy, embedding model selection, and reranking. For domain-specific tasks where off-the-shelf models underperform, we build custom models tailored to your use case. Model evaluation frameworks—ROUGE, BLEU, BERTScore, human preference evaluation—are built in from day one, not added as an afterthought. Clear scope and milestones prevent hidden costs and ensure measurable quality before production deployment.

The model can deliver impressive value in a particular business context, but when scaling across geographies, business units, or entirely new product lines, a different approach to engineering is required to achieve the same results. Our engineers apply model distillation, compressing large models into much smaller, cheaper versions that operate with much lower latency. The same model can scale across multiple applications with different prompting strategies and retrieval contexts. This multi-tenancy architecture allows us to isolate sensitive customer data while sharing an infrastructure for enterprise SaaS Gen AI features. The PoC-to-production engineering approach includes infrastructure hardening, load testing, cost optimization, and monitoring of pipelines.

Our full-stack Gen-AI product engineering covers the entire LLM layer, from the application level to UI. We design LLM orchestration, including LangChain, LlamaIndex, and custom orchestrators; build supporting applications such as APIs, authentication, data connectors, and UI; and implement output safety layers, including content filtering, factuality checks, and confidence scoring. Production monitoring tracks token usage, latency, error rates, and quality degradation detection. HaulHub’s multi-model ticketing system exemplifies this approach—not an AI experiment but a production application serving 500+ contractors daily, managing customer engagement across 70M+ records with automated jobsite classification, traffic analysis, and report generation. We build Gen AI systems that work reliably at scale for real business goals.

Properly implementing AI in enterprise-scale systems is far more than just correctly connecting APIs. Our experts are proficient in this process and leverage async processing for batch tasks and streaming responses to effectively manage latency in user-facing features. Integration with enterprise data stores (CRMs, ERPs, document management) enables RAG retrieval over your operational data. API gateway design handles multi-model architectures, routing requests to the optimal model. Fallback logic activates when LLM confidence is low, preventing bad outputs from reaching users. Audit logging ensures compliance in regulated environments. SPD Technology has integrated Gen AI under HIPAA constraints (FSA/HSA payments project) and SOC 2 requirements. Our intelligent automation workflows augment existing systems with production-grade reliability.

We apply parameter-efficient fine-tuning (LoRA, QLoRA) for most tasks—reducing GPU budget while maintaining performance—and full fine-tuning when the base model’s knowledge distribution needs a significant shift. Instruction tuning builds domain-specific AI assistants; domain-adaptive pre-training handles specialized vocabularies (medical, legal, financial terminology). Fine-tuning outperforms RAG when tasks require internalized knowledge patterns rather than retrieved context: code generation, classification, structured extraction. We evaluate every fine-tuned model against your specific task before and after training, quantifying improvement with ROUGE, BLEU, BERTScore, or custom metrics. SPD Technology includes benchmarking in every engagement—clients see measurable performance gains, not promises.

During production, the model’s effectiveness gradually fades. Output quality starts to drift, user behavior changes, and the knowledge base slowly turns stale. Our Gen AI observability includes latency dashboards and token usage, output-quality sampling pipelines that combine human review and automated evaluation, and embedding-drift detection for RAG systems. As retrieval quality drops, knowledge base freshness monitoring triggers re-ingestion. The activation of model retraining pipelines is triggered by user feedback signals and performance thresholds, such as increased hallucinations and accuracy loss. Cost anomaly alerting prevents runaway inference bills. Our MLOps and model lifecycle management ensure your Gen AI systems remain reliable, cost-effective, and aligned with business metrics long after launch.