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.
Every product we build uses at least one LLM. Several use multiple LLMs with dynamic routing selecting the optimal model per query based on complexity, cost, and compliance requirements. 15 LLM-powered products shipped across healthcare, fintech, martech, edtech, and enterprise automation. We don’t learn on your project we apply patterns proven across 15 previous ones.
Describe your feature requirements. Our team will recommend the right model, architecture, and cost projection based on your actual data, not marketing benchmarks.
Trusted in production
LLM Integration Services
LLM integration goes far beyond wrapping an API call in a chat interface. Production LLM features require prompt architecture, streaming infrastructure, cost management, safety guardrails, provider abstraction, and output validation — each one a distinct engineering discipline.
The LLM landscape evolves every 3–6 months. New models launch. Pricing changes. Capabilities shift. The product that survives is the one designed for provider flexibility built on an abstraction layer that lets you switch models, add new providers, and adjust routing logic without rewriting your application.
TechEniac builds every LLM integration on this principle of provider independence. Your application calls a unified interface. The abstraction layer handles everything else provider selection, authentication, request formatting, response parsing, error handling, and automatic failover. When OpenAI raises prices, when Claude adds a capability you need, when a provider has an outage switching is a configuration change, not a rewrite.
Our LLM Integration Services
Six capability areas spanning model evaluation, provider architecture, streaming, safety, cost optimisation, and multi-model routing each engineered for production reliability across every LLM provider in the market.
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.
SolidHealth AI's evaluation revealed Gemini 1.5 Pro outperformed GPT-4o on medical reasoning while costing 60% less per query. But Llama 3.3 via Groq was sufficient for 40% of queries at one-third Gemini's cost. This evaluation directly shaped the routing architecture that saved 40% on inference costs.
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.
SolidHealth AI switches between Vertex AI (Gemini) and Groq (Llama) based on three signals: query complexity, current provider latency, and monthly cost budget consumption. When one provider experiences elevated latency, the system routes to the alternative automatically zero user impact, zero downtime.
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.
SolidHealth AI uses bidirectional WebSockets with parallel text and audio streaming the user sees the text response appearing while simultaneously hearing the audio version being generated. Under 100ms time-to-first-token. ContentForge AI streams content generation across 12 formats in real time.
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.
ContentForge AI's 3-stage compliance pipeline catches 97% of violations. EduAssist AI refuses questions outside course materials in 99.3% of cases. ScribeAI never generates ICD-10 codes from free text clinical coding comes from validated ontology.
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.
SolidHealth AI saved 40% on inference costs via dynamic Gemini/Llama routing. MortgageLens AI's query cache reduced repeat lookups by 60%. Every TechEniac LLM integration includes all three optimisations as standard.
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.
ScribeAI combines three models in a single pipeline: Whisper Large-v3 for bilingual Arabic-English transcription, GPT-4o for clinical NLP processing, and Azure Cognitive Services as real-time STT fallback. ContentForge AI routes between GPT-4o (creative content), Claude (compliance-sensitive), and AraBART (Arabic generation) automatically per content type.
LLM Providers We Work With
We don’t recommend LLMs based on marketing materials. We recommend based on evaluation against your specific queries measured on accuracy, latency, cost, and output consistency.
Strongest complex reasoning and creative content generation. The benchmark model for multi-step analysis, persuasive writing, and content production.
SolidHealth AI (screening scoring), ContentForge AI (long-form content), CourseGen AI (curriculum generation), ScribeAI (clinical NLP), TalentSync AI (candidate evaluation)
Best instruction-following and compliance-sensitive output. Lowest hallucination rate for structured data extraction. The default for regulated industries.
ClaimBot (FCA-compliant claims extraction), ContentForge AI (regulated content), EduAssist AI (grounded generation with mandatory citation), WealthPilot AI (FCA boundary classification)
Strong multimodal processing (text + image + document understanding). Competitive pricing. GCP-native integration for Vertex AI deployments.
SolidHealth AI (primary reasoning model), Linkfluencer (vision-based content verification), TalentSync AI (resume parsing), MortgageLens AI (embeddings)
Lowest-cost inference for straightforward tasks. High-throughput processing. Deployed via Groq for fast inference speeds.
SolidHealth AI (simple health lookups at one-third Gemini's cost via dynamic routing)
Speech-to-text transcription including bilingual Arabic-English with code-switching detection. Fine-tunable for domain-specific audio.
ScribeAI (clinical consultation transcription, fine-tuned on UAE medical recordings), ClaimBot (voice claims channel)
Our Development Approach
LLM integration is not a single API call it is a multi-layer engineering discipline covering model selection, provider architecture, streaming, safety, cost management, and ongoing optimisation. Here is how we approach every LLM integration engagement.
A data-driven model recommendation based on 50–100 representative queries from your domain with measured accuracy, latency, cost per query, and output consistency across GPT-4o, Claude, Gemini, and Llama.
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.
A production-ready abstraction layer that isolates your application from LLM API specifics enabling runtime provider switching, automatic failover, and configuration-based model changes without code deployment.
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.
Token-level streaming infrastructure delivering under 100ms time-to-first-token using Server-Sent Events for web applications or WebSockets for bidirectional real-time communication.
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.
A multi-layer safety system covering input validation, output validation, hallucination prevention, and cost controls appropriate to your domain's risk level and compliance requirements.
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. ContentForge AI catches 97% of compliance violations. EduAssist AI declines out-of-material questions 99.3% of the time. These aren't optional add-ons they're production requirements.
A cost-optimised LLM deployment with model routing, response caching, token budgeting, and production monitoring with ongoing cost tracking and optimisation recommendations.
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 they pay for themselves within 3–6 months of production use.
Our evaluation uses your actual queries not benchmarks to recommend the right model, the right architecture, and a realistic cost projection.
Technology
Complex reasoning, creative generation, content production. The benchmark for multi-step analysis.
Compliance-sensitive output, structured extraction, instruction-following. Default for regulated industries.
Multimodal processing, competitive pricing, GCP-native integration.
Lowest-cost inference for straightforward tasks. High throughput.
Speech-to-text with bilingual Arabic-English and code-switching detection.
Unified interface isolating your application from LLM API specifics. Runtime switching without restarts.
Complexity-based, cost-based, and latency-based routing selecting the optimal provider per query.
Sub-500ms rerouting when a provider degrades. Zero user impact.
Unidirectional token streaming for web applications. Under 100ms time-to-first-token.
Bidirectional real-time communication for applications requiring parallel streams (text + audio).
Frontend components that display tokens as they arrive typing indicators, progressive rendering, and graceful error states.
Prompt injection detection, adversarial input filtering, topic boundary enforcement.
Compliance checking, format verification, confidence thresholds, citation verification.
Cached results for identical or semantically similar queries. Redis-backed with configurable TTL.
Per-user and per-tenant monthly limits with graceful degradation and usage dashboards.
Results in Production
LLM integrations powering real features for real users across healthcare, marketing, education, insurance, and clinical documentation. Not prototypes. Not demos. Production.
Runtime switching between Gemini 1.5 Pro and Llama 3.3 based on query complexity, latency, and cost signals. Simple queries route to Llama (one-third cost). Complex medical reasoning routes to Gemini. Automatic failover on provider degradation.
GPT-4o for long-form content and persuasive ad copy. Claude Sonnet for compliance-sensitive regulated content. AraBART for Arabic language generation. Model routing selects the appropriate provider per content type automatically.
Whisper Large-v3 for bilingual Arabic-English transcription. GPT-4o for clinical NLP processing (entity extraction, ICD-10 mapping, SOAP note generation). Azure Cognitive Services as real-time STT fallback. Three models, one pipeline, each selected for its strength.
Claude Sonnet with strict grounding rules generating responses exclusively from course materials with mandatory source citation. Relevance thresholds decline queries outside course content.
Model selection, provider lock-in, outage handling, cost control, fine-tuning the questions every founder asks before shipping an LLM-powered feature.
No outages when a provider goes down. No surprise bills at the end of the month. No hallucinated responses that embarrass your brand. Production-grade LLM integration means reliability, cost control, and safety engineered from Day 1.