Generative AI Engineering
Build Custom Generative AI Solutions for the Enterprise
We help businesses integrate, fine-tune, and deploy custom LLMs and Generative AI agents to automate complex workflows with technical precision.
40%
Faster Delivery
Proprietary AI scaffolding accelerated by the GeeksForce AI-Native Engine.
SOC2
Compliant
Enterprise-grade security protocols baked into every AI layer and deployment.
30+
Multi-Model
Seamless integration with GPT-4, Claude 3, Llama 3, and specialized models.
Our Specialization
Technical AI Solutions
Engineered for stability and scale, our services span the entire Generative AI landscape from base model tuning to complex agentic orchestration.
Custom LLM Development & Fine-Tuning
Domain-specific model optimization using PEFT, LoRA, and QLoRA techniques for specialized enterprise data.
- Hyperparameter Tuning
- Reward Model Training
- Quantization Pipelines
RAG (Retrieval-Augmented Generation)
Connecting LLMs to your private data silos with sub-second vector search and contextually aware retrieval.
- Semantic Search Layers
- Vector DB Orchestration
- Hybrid Search Ranking
AI Agentic Workflows
Designing autonomous agents capable of tool-use, multi-step reasoning, and complex task completion.
- Tool-Calling Logic
- Self-Correction Loops
- Multi-Agent Swarms
Our Technical Core Stack
Efficiency Multiplied
The GeeksForce
AI-Native Engine
We don’t just build AI; we use AI to build. Our proprietary software factory integrates LLMs at every stage of the development lifecycle, from automated unit testing to synthetic data generation.
Automated Scaffolding
AI-generated architectural foundations reduce manual setup time by 70%.
Continuous Evaluation
Automated “LLM-as-a-judge” testing ensures model outputs remain grounded and relevant.
Security First Architecture
Enterprise-Grade Trust
AI implementation without risk. We prioritize data sovereignty and regulatory alignment in every deployment.
Data Sovereignty
Deploy models in your private cloud (VPC) so data never leaves your environment.
Compliance Ready
HIPAA, GDPR, and SOC2 compliant architectures for sensitive sector applications.
Bias Mitigation
Advanced adversarial testing and red-teaming to eliminate harmful or biased outputs.
PII Masking
Automated scrubbing of personally identifiable information before it hits model layers.
The 6-Step Lifecycle
01
Discovery
Identifying use cases with high ROI and technical feasibility within your existing infrastructure.
02
Architecture
Designing the multi-model stack, orchestration layer, and security protocols.
03
Data Engineering
Cleaning, labeling, and vectorizing datasets for ingestion and fine-tuning.
04
Model Tuning
Refining parameters and RLHF integration to ensure the model aligns with domain expertise.
05
Integration
Connecting AI capabilities into production APIs, frontend apps, and internal tools.
06
Scaling
Optimizing for latency, managing GPU compute costs, and monitoring model drift.
Common Technical Queries
Technical answers for executive decisions.
We use private VPC deployments where data never crosses external APIs. We also implement differential privacy techniques and PII masking to ensure that models do not “memorize” sensitive information during the training or retrieval phases.
POCs are typically delivered within 4-6 weeks. Full-scale production deployment including integration with complex tool-chains and rigorous safety testing usually takes 3-5 months depending on complexity.
We implement prompt optimization, semantic caching, and model routing. Our router identifies tasks that can be handled by cheaper models (like Llama 3 8B) versus those requiring premium models (like GPT-4o), reducing costs by up to 60%.
Initialize Your AI Transformation
Ready to modernize? Fill out the deployment brief below and our senior engineering team will conduct a feasibility review.
