LLM Discovery & Scoping
Two-week scoping sprint: identify the AI use case, pick the model, define eval criteria.
LLM Integration Services
Plug GPT-5, Claude Opus 4.7, Gemini 2.5, or open-source models into the products your customers already use. We design the gateway, the prompts, the eval suite, and the cost controls so your LLM features ship in weeks and stay reliable in production.
120+
LLM integrations shipped
2-3 wks
Typical integration timeline
99.9%
Production uptime SLA
60%
Average inference cost cut
Production LLM integration done right
1
Multi-provider gateway
- Single SDK abstraction over OpenAI, Anthropic, Google, AWS Bedrock
- Automatic failover when a provider degrades
- Per-route model routing (cheap for simple, premium for hard)
- Bring-your-own-key support for enterprise customers
2
Retrieval-augmented generation (RAG)
- Document ingestion pipelines with chunking and metadata
- Vector storage on Pinecone, Weaviate, Qdrant, or pgvector
- Hybrid search combining BM25 and embeddings
- Citation tracking so users see the source
3
Tool calling and agents
- Type-safe tool schemas via Pydantic or Zod
- Multi-step agent loops with retry and human-in-the-loop
- Tool sandboxing for code execution and file IO
- Trace UIs your support team can debug from
4
Evaluation and quality control
- Golden test suites that run on every prompt change
- LLM-as-judge scoring with confidence thresholds
- Prompt versioning and A/B rollout
- Drift alerts when accuracy slips
5
Cost and rate-limit guardrails
- Per-tenant token budgets and hard caps
- Semantic caching to dedupe similar queries
- Streaming with prompt caching to cut cost 70-90%
- Real-time dashboards showing dollars per feature
6
Compliance and data handling
- PII redaction at the gateway layer
- SOC 2 / HIPAA / GDPR-friendly request flows
- Zero-retention configurations for regulated industries
- Audit trails of every prompt and response
Our LLM integration services
From first prompt to production-grade AI features
Gateway & SDK Build
Multi-provider gateway with rate limiting, caching, and observability built in.
RAG System Implementation
Document ingestion, vector search, hybrid retrieval, and citation rendering.
Agent & Tool Development
Tool-calling agents with sandboxed execution and human-in-the-loop fallbacks.
Eval & QA Engineering
Regression test suites, LLM-as-judge scoring, prompt rollout pipelines.
Migration & Optimization
Move from OpenAI to multi-provider, cut costs, hit new latency SLOs.
Why teams pick SpeedMVPs for LLM integration
Vendor-neutral by design
Swap providers in hours when pricing or quality changes — no rewrite required.
Eval-first, prompt-second
We define the test suite before writing the prompt. No vibes-based shipping.
Cost-aware architecture
Caching, model routing, and budgets baked in from day one — not bolted on later.
Observability included
Token counts, latency, cost, and quality metrics ship as Grafana dashboards.
Streaming UX out of the box
Tokens, tool events, and progress indicators stream to the client cleanly.
Handoff-ready code
Documentation, runbooks, and architecture diagrams ship with every project.
Why teams pick SpeedMVPs for LLM integration
Vendor-neutral by design
Swap providers in hours when pricing or quality changes — no rewrite required.
Eval-first, prompt-second
We define the test suite before writing the prompt. No vibes-based shipping.
Cost-aware architecture
Caching, model routing, and budgets baked in from day one — not bolted on later.
Observability included
Token counts, latency, cost, and quality metrics ship as Grafana dashboards.
Streaming UX out of the box
Tokens, tool events, and progress indicators stream to the client cleanly.
Handoff-ready code
Documentation, runbooks, and architecture diagrams ship with every project.
LLM integration — FAQ
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Portfolio: AI Products Built for Global Startups
From content platforms and AI assistants to analytics dashboards and fintech solutions—see how we've transformed ideas into production-ready MVPs in 2-3 weeks across diverse industries. Each product launched successfully, serving users globally.
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