Put GPT, Claude, or Gemini inside the product you already have
LLM Integration
LLM integration is the engineering work of embedding large language models — GPT, Claude, Gemini, or open-weight models — into an existing software product: API orchestration, retrieval-augmented generation, prompt and eval pipelines, cost routing, and monitoring. AI Pinnacle retrofits AI features into production SaaS, mobile apps, and ERPs without re-platforming.
What you get
- Feature scoping: which workflows benefit from LLMs, with projected run-costs
- RAG architecture (pgvector/Pinecone) with retrieval quality evals
- Prompt + eval pipeline so releases don't regress quality silently
- Cost routing: cheap-model-first with escalation to frontier models
- Streaming UX, caching, and rate-limit handling in your existing stack
- Security review: prompt-injection defenses, PII redaction, tenant isolation
How much does LLM integration cost?
A single production feature (AI search, summarization, drafting) runs USD 8K–18K and ships in 3–6 weeks. A full RAG platform over your proprietary data runs USD 30K–70K. Enterprise rollouts with SSO, audit, and multi-region residency run USD 80K–180K.
Fine-tuning, RAG, or prompt engineering — which do you need?
Start with prompt engineering, add RAG when answers must be grounded in your data, and fine-tune only when you need consistent format/tone at high volume. This is the cost order too: prompts are near-free, RAG adds ~USD 200–900/month in vector infrastructure, fine-tuning adds training runs plus evaluation overhead.
- •Prompt engineering: hours to days; fixes 60% of quality issues
- •RAG: grounds answers in your documents; kills most hallucination complaints
- •Fine-tuning: format consistency and domain tone at scale; last resort, not first
How do you stop LLM run-costs from exploding?
Route by difficulty. Our standard pattern sends the cheap model (GPT-5 mini, Claude Haiku) first and escalates only failed or flagged requests to frontier models — cutting inference bills 40–70% versus frontier-only deployments, with response caching and prompt compression on top.
Will an LLM feature leak our customer data?
Not if the integration is engineered for isolation: zero-retention API tiers, PII redaction before the prompt leaves your VPC, tenant-scoped retrieval, and prompt-injection filtering on any user-supplied content. We implement all four as standard, and we sign NDAs before the first architecture call.
Engagement tiers & pricing
Every proposal includes a projected monthly inference budget for your traffic — approved before a line of code is written.
Feature Integration
USD 8K–18K
3–6 weeks
- One production AI feature
- Prompt + eval suite
- Cost model before build
- Streaming UX
RAG Platform
USD 30K–70K
6–12 weeks
- Multi-source ingestion
- Vector DB + retrieval evals
- Model routing + caching
- Admin analytics
Enterprise Rollout
USD 80K–180K
3–6 months
- SSO + audit logging
- Multi-region data residency
- Fine-tuning where justified
- Vendor-failover architecture
- Compliance documentation (GDPR/HIPAA)
Frequently asked questions
Can you integrate AI into our existing product without a rewrite?
Yes — that is the core of this service. We embed LLM features into your current React/Node/Django/Rails/mobile stack behind your existing auth and infra. Most feature integrations touch fewer than a dozen files in your codebase.
Which model should we use — GPT, Claude, or Gemini?
It depends on the task mix: we benchmark your actual prompts across models during week one and pick per-task winners. Most production systems we ship route between two providers for cost and availability rather than betting on one.
How do you test LLM features before release?
With an eval pipeline: a golden set of real inputs scored automatically on every change (accuracy, groundedness, format, safety). No prompt or model change ships without passing the eval gate — the LLM equivalent of a CI test suite.
What does an LLM feature cost to run monthly?
Typical mid-market deployments run USD 380–780/month for a single feature with cheap-model routing, and USD 1,800–4,200/month for high-volume support workloads. We model this for your traffic before you commit to the build.
Do you work with open-source models for data-sensitive workloads?
Yes. Where residency or confidentiality rules out API models, we deploy Llama/Mistral-class models in your VPC — usually behind the same routing layer, so you keep frontier quality for non-sensitive paths.
Related insights
Generative AI ROI: 2026 Enterprise Benchmarks Across 40 Deployments
Real payback windows, cost-per-token economics, and the three deployment patterns that actually clear CFO scrutiny in 2026.
AI Agents vs RAG: Which Architecture Wins for Enterprise in 2026?
Agentic frameworks (LangGraph, CrewAI, OpenAI Agents SDK) vs classic RAG: when each wins, when each fails, and the hybrid pattern we ship.
Building HIPAA-Compliant AI Pipelines
How we architected a zero-trust data pipeline for a US telehealth platform processing 2M+ patient records.
Scope your llm integration project this week
NDA-first discovery call, fixed-price statement of work inside 5 business days, and 100% IP transfer on completion.
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