Solution Design

Analyze Requirements &
Design Solutions

We align architectural designs with specific business needs, validate feasibility through rapid Proof-of-Concepts, and ensure consistent implementation using standardized components.

Design Strategy

Analyze Requirements &
Design Solutions

We analyze your business needs (latency, cost, privacy) and technical constraints to select the optimal Integration Patterns and Deployment Strategies. There is no "one size fits all"—we map constraints to blueprints.

Define Your Constraints

Recommended Architecture
Low Latency Pattern

Edge / Hybrid AI

Caching and small models at the edge for instant response, falling back to cloud for reasoning.

Client
Gateway
Logic
Edge Cache
Small Language Model
Async Processing
Model Selection

Choosing the
Right Model

Framework for selecting the best LLM for your use case based on capability, cost, and latency requirements.

Model Candidates

Claude 3.5 Sonnet
$
Anthropic (Bedrock)
Titan Text Premier
$
AWS (Native)
Llama 3.1 70B
$ (Infra only)
Meta (Self-Host/Bedrock)
Gemini 3 Pro (Preview)
$
Google (Vertex/AI Studio)
Model Card Analysis

Gemini 3 Pro (Preview)

Google (Vertex/AI Studio)

Key Strengths

Massive Context (2M+)Native ThinkingMultimodal

Tech Specs

Context Window2M+
Ideal Use CaseComplex Agentic Workflows & Reasoning

Why we might choose this?

Massive context window and native multimodal capabilities for analyzing video and codebases.

Model Abstraction Layer

We decouple your application logic from specific models, allowing you to swap providers (e.g., OpenAI to Bedrock) instantly via configuration.

  • Swap providers (e.g., OpenAI to Bedrock) instantly via config.
  • No code deployments required to change models.

Fine-Tuning

Using LoRA/QLoRA adapters to customize model behavior for your specific domain vocabulary and style without full retraining.

  • LoRA/QLoRA adapters for cost-effective training.
  • Hosted in SageMaker or Bedrock Custom Models.
PoC Validation

The PoC Factory

Before committing to full-scale deployment, we rigorously validate Feasibility, Performance, and Business Value. Our standardized PoC framework prevents expensive failures by proving the solution works on your data, not just in a demo.

Hypothesis Analysis

Phase 1/4

Success Criteria

  • User Intent Accuracy > 85%
  • Latency < 2s
Business Alignment
Mapping GenAI capabilities to specific ROI targets.
Outcome
Iterating to validate assumptions...
Standardization

Standardized Technical
Components

To ensure consistent implementation across multiple deployment scenarios, we utilize a Standardized Component Library and adhere to the GenAI Well-Architected Framework. This reduces technical debt and accelerates time-to-market.

GenAI Well-Architected Framework

We don't start from scratch. We utilize a Standardized Blueprint that adheres to the Well-Architected Framework pillars: Operational Excellence, Security, Reliability, Performance Efficiency, and Cost Optimization.
Security
Reliability
Performance
Governance