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Services · LLM Integration & Development

LLM Integration & Development Production-Grade Language Model Features for Your Product

TechEniac integrates GPT-4o, Claude, Gemini, and Llama into SaaS products with the production reliability that prototype-grade integrations lack. We build LLM-powered features with provider abstraction, dynamic model routing, streaming infrastructure, safety guardrails, and cost optimisation engineering work that determines whether your LLM feature delights users or embarrasses your brand.

15
LLM-powered products shipped
40%
AI cost reduction (SolidHealth AI)
99.9%
Uptime with provider failover
100ms
Time-to-first-token streaming

Trusted to build

SolidHealth AI
ContentForge AI
ScribeAI
CourseGen AI
ClaimBot
TalentSync AI
EduAssist AI
MortgageLens AI

Capabilities

What TechEniac Builds

LLM Provider Evaluation & Selection

We evaluate GPT-4o, Claude, Gemini, and Llama against your specific requirements not industry benchmarks. We test with 50–100 representative queries from your domain, measuring accuracy, latency, cost per query, and output consistency. The recommendation is backed by your actual data.

Provider Abstraction & Runtime Switching

A unified interface that isolates your application from LLM API specifics. Provider selection, authentication, request formatting, response parsing, error handling, and failover all handled by the abstraction layer. Runtime provider switching without service restarts.

Real-Time Streaming Infrastructure

LLM responses take 2–15 seconds to generate completely. Streaming delivers tokens as they are generated text appears word-by-word, creating a conversational experience instead of a loading screen. Under 100ms time-to-first-token latency.

Safety Guardrails & Output Validation

Four layers of protection that prototype-grade integrations lack. Input validation blocking prompt injection, filtering adversarial inputs, enforcing topic boundaries. Output validation checking against compliance rules, accuracy requirements, and format specifications. Hallucination prevention RAG grounding, citation enforcement, confidence thresholds. Cost controls per-user and per-tenant token budgets.

Cost Optimisation & Model Routing

Three strategies that reduce LLM costs by 30–50% without compromising quality. Model routing simple queries go to cheaper models, complex queries go to powerful models. Response caching identical or similar queries return cached results. Token budgeting per-user and per-tenant limits with graceful degradation.

Multi-Model Pipeline Architecture

Different features need different models. One feature needs GPT-4o's reasoning. Another needs Claude's structured output. A third needs Gemini's cost efficiency. We build pipelines where multiple models work together each selected for its specific strength within the workflow.

OpenAI (GPT-4o, GPT-4o-mini)

Strongest complex reasoning and creative content generation. The benchmark model for multi-step analysis, persuasive writing, and content production. Used in SolidHealth AI (screening scoring), ContentForge AI (long-form content), CourseGen AI (curriculum generation), ScribeAI (clinical NLP), TalentSync AI (candidate evaluation).

Anthropic (Claude Sonnet)

Best instruction-following and compliance-sensitive output. Lowest hallucination rate for structured data extraction. The default for regulated industries. Used in ClaimBot (FCA-compliant claims extraction), ContentForge AI (regulated content), EduAssist AI (grounded generation with mandatory citation), WealthPilot AI (FCA boundary classification).

Google (Gemini 1.5 Pro)

Strong multimodal processing (text + image + document understanding). Competitive pricing. GCP-native integration for Vertex AI deployments. Used in SolidHealth AI (primary reasoning model), Linkfluencer (vision-based content verification), TalentSync AI (resume parsing), MortgageLens AI (embeddings).

Meta (Llama 3.3 via Groq)

Lowest-cost inference for straightforward tasks. High-throughput processing. Deployed via Groq for fast inference speeds. Used in SolidHealth AI (simple health lookups at one-third Gemini's cost via dynamic routing).

OpenAI (Whisper Large-v3)

Speech-to-text transcription including bilingual Arabic-English with code-switching detection. Fine-tunable for domain-specific audio. Used in ScribeAI (clinical consultation transcription, fine-tuned on UAE medical recordings), ClaimBot (voice claims channel).

Delivery process

How We Work

  1. 01

    Phase 01: LLM Evaluation & Selection

    We evaluate against your specific requirements, not industry benchmarks. SolidHealth AI's evaluation revealed Gemini outperformed GPT-4o on medical reasoning at 60% lower cost but Llama handled 40% of queries at one-third Gemini's cost. These findings directly shaped the production architecture. No guesswork. No assumptions. Data.

  2. 02

    Phase 02: Provider Abstraction Layer

    Your application calls a unified interface. The abstraction layer handles provider selection, authentication, request formatting, response parsing, error handling, and failover. SolidHealth AI switches between Gemini and Llama in under 500ms when a provider degrades. Without this layer, a provider outage means your entire AI feature goes down.

  3. 03

    Phase 03: Streaming & Real-Time Infrastructure

    LLM responses take 2–15 seconds to generate. Streaming eliminates the loading screen by delivering tokens as they are generated. SolidHealth AI streams text and audio simultaneously via bidirectional WebSockets. ContentForge AI streams content generation across 12 formats. The user sees progress immediately instead of staring at a spinner.

  4. 04

    Phase 04: Safety Guardrails & Output Validation

    Four layers of protection. Input validation blocks prompt injection and enforces topic boundaries. Output validation checks against compliance rules and format specifications. Hallucination prevention grounds responses via RAG, enforces citations, and implements confidence thresholds. Cost controls set per-user and per-tenant token budgets. These aren't optional add-ons they're production requirements.

  5. 05

    Phase 05: Cost Optimisation & Ongoing Management

    LLM costs scale linearly with usage. Model routing sends simple queries to cheap models and complex queries to powerful models saving 30–50%. Response caching returns stored results for identical queries MortgageLens AI reduced repeat lookups by 60%. Token budgets prevent runaway costs. These three optimisations are standard in every TechEniac LLM integration.

Tech Stack

Technologies We Use

LLM providersGPT-4o / GPT-4o-mini (OpenAI), Claude Sonnet (Anthropic), Gemini 1.5 Pro (Google), Llama 3.3 (Meta via Groq), Whisper Large-v3 (OpenAI)
Provider architectureProvider Abstraction Layer, Dynamic Model Routing, Automatic Failover (<500ms)
Streaming infrastructureServer-Sent Events (SSE), WebSockets, Streaming Response Rendering
Safety & cost managementInput Validation, Output Validation, Response Caching (Redis), Token Budgeting

Why TechEniac

Our Approach

Provider abstraction

Switch models, add providers, adjust routing without rewriting your application.

Automatic failover

When one provider degrades, traffic reroutes in under 500ms. Zero user impact.

Cost intelligence

Model routing, response caching, and token budgets so costs don't surprise you at scale.

Frequently Asked Questions

Ready to build with TechEniac?

Book a free 30-minute strategy session. We’ll review your product idea, discuss architecture options, and map a realistic path from idea to launch.