Inventiple integrates large language models into existing software for B2B SaaS, enterprise teams, and AI-native startups. Cost controls, evaluation harnesses, observability, and a provider-agnostic architecture so you're never locked to one model. Senior engineers, fixed price, 2-8 week delivery.
Adding a language model to existing software looks deceptively simple. Sign up for an OpenAI key, wire a few API calls, ship the feature. The demo works. The internal beta works. Then real users hit it at scale and three classes of problem surface within the first month.
Cost explodes silently. A handful of users with creative prompts can quietly run up four-figure bills overnight. Long prompts get longer. Context windows fill. Token counts spiral. Without per-request and per-user budget caps enforced before the model call, finance finds out the hard way. We've inherited engagements where a single feature was costing $40,000/month before anyone noticed.
Latency degrades as adoption grows. What worked at 100 requests/day breaks at 100,000. Naive integrations don't use streaming, don't batch independent calls, don't cache repeat prompts, and don't tier models by task. Users wait 8 seconds for an answer that could have arrived in 1.
Provider lock-in becomes a strategic liability. Most LLM integrations are written against a specific provider's SDK with prompts tuned for that provider's quirks. When the price shifts, when a better model arrives, when a customer requires a different provider for compliance — the team faces a multi-week refactor instead of a configuration change. We've seen this kill renewals.
Evaluation is non-existent. Without a labeled test set and an automated harness, every prompt change is deploy-and-pray. Was last week's tweak an improvement? Nobody knows. Did the model upgrade preserve quality? Nobody knows. Without measurement, quality drifts downward over time.
The teams that ship LLM features successfully in 2026 — at any company, any size — share one pattern: they treat LLM integration as production engineering, not API wiring. We exist to bring that pattern to teams that haven't built it before.
Every LLM integration we ship is structured around five non-negotiables. None of them are optional, regardless of engagement size.
Every integration sits behind an internal abstraction we call the model swap layer. Prompts, tools, and evals are defined once. The provider behind them is a configuration change. When GPT-5.5 ships, or when Anthropic releases Claude 4.5, or when an open-source model becomes cost-effective — you swap providers in hours, not weeks.
Every LLM call passes through three checks: per-request token cap, per-user daily budget, and per-organization monthly budget. Caps are enforced before the model is called, not after. Real-time dashboards show spend by feature, user, and time. Cost runaways aren't possible by architecture.
Before any prompt is tuned, we build a labeled evaluation set with your team and a regression harness that runs it on every change. Braintrust, LangSmith, or our internal framework. Quality movement is measurable. Quality regressions are caught before deployment.
Streaming responses, prompt caching, model tiering (small/fast for simple tasks, large/capable for complex ones), parallel calls when independent, and edge response caching where appropriate. We architect for production latency budgets, not best-case API response times.
Helicone or LangFuse instrumentation on every call. You see latency distributions, cost trends, which prompts fail, which users invoke which features, where the integration is slow. When something breaks at 2 AM, root cause is minutes away, not days.
Three shapes of LLM integration work, depending on scope.
One LLM-powered capability added to an existing product — e.g., AI-generated summaries, smart classification, structured extraction, content rewriting. Includes the model swap layer, cost controls, basic eval harness, and observability. Best for: teams adding their first production AI feature.
Several LLM-powered capabilities across the product. Full evaluation suite, multi-model tiering, prompt caching, advanced cost controls, admin dashboards. Best for: SaaS teams making AI a meaningful part of the product surface area.
Multi-tenant LLM integration with per-customer model selection, compliance scaffolding (HIPAA, SOC 2), audit logs, on-prem or VPC deployment options, governance dashboards. Best for: enterprises rolling out LLM capabilities across multiple internal tools or customer products.
Fixed-price ranges per engagement type, quoted after discovery.
One LLM capability, production-ready.
Multiple LLM features, full production stack.
Multi-tenant, compliance, governance.
Provider-agnostic by design. We pick by fit, not vendor relationship.
Production LLM integration is far more than wiring an API key. It includes: model selection (which provider for which task), prompt engineering and versioning, cost controls and budget caps, observability (which prompts cost what, which fail), evaluation harnesses, fallback behavior when models are down or slow, PII redaction, safety guardrails, latency optimization, caching strategies, and a swap layer so you're not locked to one provider. Skipping any of these is what turns a working pilot into a production incident.
They can, and many do — and most production LLM incidents we've inherited come from teams that took this path without specialized experience. Cost overruns from unbounded prompts. Latency spikes from naive API patterns. Hallucinations in user-facing surfaces. Provider lock-in that becomes painful when prices change. We bring the patterns we've shipped a dozen times before. Your team can absolutely run it after we set the foundation right.
All of them, by design. We routinely integrate Anthropic Claude (Sonnet, Opus, Haiku), OpenAI GPT-5 and o-series, Google Gemini, Mistral, and open-source models via vLLM or Together AI when self-hosting is required. For specific use cases we also integrate domain-tuned providers (e.g., medical LLMs, code LLMs). The integration is architected so the provider is a configuration choice, not a hard dependency.
A focused single-feature integration (one LLM-powered capability inside an existing product) typically costs $20,000-$40,000 over 2-3 weeks. A broader integration with multiple capabilities, evaluation, observability, and cost controls ranges $40,000-$80,000 over 4-6 weeks. Enterprise integrations with compliance scaffolding, multi-tenancy, audit logs, and on-prem deployment range $80,000-$150,000 over 6-10 weeks.
Three layers: (1) hard budget caps per request, per user, and per organization, enforced before any model call, (2) prompt and context length caps to prevent unbounded inputs, (3) real-time cost dashboards via Helicone or LangFuse so you see spend by feature, by user, by hour. We've never had a client experience a cost runaway — because the architecture makes them impossible.
Yes. Every LLM integration respects your existing auth — user permissions flow through to which data the LLM can access, which actions it can take, and which results it returns. PII redaction at ingestion and output verification before display are standard. For regulated environments we use BAA-eligible providers (Azure OpenAI Service, AWS Bedrock with proper configuration) or self-hosted models.
Latency optimization patterns we routinely apply: streaming responses (users see tokens as they generate), prompt caching (Claude's prompt caching cuts repeat-prompt costs and latency dramatically), model tiering (use smaller faster models for simple tasks, larger models only when needed), parallel calls when independent, and response caching at the edge for repeat queries. Most production LLM features can hit sub-1s perceived latency with the right architecture.
Every LLM integration ships with an evaluation harness using Braintrust, LangSmith, or our internal framework. A labeled test set built with your team runs on every prompt change, model swap, or pipeline tweak. You see quality movement on real cases, not vibes. For features in production, telemetry from real usage feeds back into the eval set over time, so the harness keeps getting more representative.
Yes — LLM integration often involves RAG, agents, or both. If your integration needs retrieval, we build the RAG pipeline alongside. If it needs multi-step reasoning or tool use, we architect the agentic layer. We have dedicated pillar pages on our RAG and MCP/Agentic AI services for those engagement shapes. For simpler integrations (no retrieval, no agents — just prompts and responses), this is the right service line.
30 days of post-launch support included. Model providers release new versions frequently — most clients move to a quarterly model-refresh retainer ($5K-$15K/quarter) so the integration stays current. Larger clients move to a monthly engineering retainer ($15K-$40K/mo) for ongoing tuning, new feature additions, and eval-set expansion as real usage patterns emerge.