The problem we solve
A retrieval demo over a folder of PDFs is a weekend project. A system that reads live order data, respects per-user permissions, writes back to a production database, and survives a hostile prompt is an engineering problem — and that gap is where most AI initiatives stall. AI delivers value only when it connects safely to real business data, and most data architectures were never designed to expose themselves to a probabilistic client.
Teams tend to fail in one of two directions. Some hand the model over-broad credentials — a service account that can read everything — and inherit a new vulnerability class, where a prompt injection hidden in a customer email can walk out with the customer table. Others sandbox the model so aggressively that it can only answer questions about stale exports, and the AI feature quietly becomes a FAQ bot nobody opens twice. Both outcomes share a root cause: the integration layer between the model and the business systems was treated as glue code instead of as the product.
We treat that layer as the product. Evolvier builds LLM applications and configures secure Model Context Protocol (MCP) servers so AI agents can read and act on your systems through typed, scoped, audited gateways. It is systems engineering first: least-privilege access, validation at every tool boundary, and observability over every call the model makes.
What production-grade LLM integration involves
The capability cards above summarize the offer; this is what each one means at the engineering level.
MCP server environments
MCP gives agents a standard way to discover and invoke tools and resources — which means the security model lives in a server you control, not in the model's goodwill. We design MCP servers as structured gateways over your internal systems: every tool is a typed contract with explicit inputs, server-side authorization checks, and rate limits. Read paths and write paths are separated, and mutating tools can require human confirmation before they execute. Each invocation is logged with caller identity, arguments, and result, so you can audit what an agent actually did rather than reconstruct it from guesswork.
The failure modes we engineer against are specific: prompt-injection-driven data exfiltration, agents acting with broader permissions than the user they serve, and destructive writes triggered by ambiguous instructions. Servers ship as containerized Node.js or Python services deployed behind your existing identity provider, following the zero-trust, token-based authentication posture described on our security page.
LLM application development
RAG pipelines, copilots, and assistants grounded in your content — with the unglamorous parts done properly. That means an ingestion pipeline that handles document updates and deletions instead of accumulating stale chunks, chunking tuned to your content rather than a default splitter, hybrid retrieval (vector plus keyword) against a vector database, and answers that cite their sources so users can verify instead of trusting blindly. We build on OpenAI and Anthropic APIs behind a provider abstraction, so switching models is a configuration decision, not a rewrite.
Evaluation is the other half of the work. Before anything widens past a pilot group, we establish golden question sets and regression tests for prompts and retrieval, plus explicit budgets for latency and per-request cost. Streaming responses, graceful fallbacks when retrieval comes back thin, and response caching are standard. The result is an application you can change with confidence — swap a model, re-chunk a corpus — and know quickly whether quality regressed.
AI readiness & integration
The highest-leverage work often happens before any model is called. Most internal systems were built for humans and dashboards, not agents: critical context lives in tribal knowledge, APIs are undocumented, and permissions are coarse where they need to be granular. Our readiness work prepares that foundation — normalizing data sources, defining the API surface agents will consume, documenting schemas and business rules in machine-readable form, and identifying which workflows can be automated at acceptable risk.
The deliverable is not a slide deck. It is a prioritized integration map: which systems to expose first, through which interfaces, behind which guardrails, and what each step unlocks next. Because agents are only as capable as the APIs underneath them, this work frequently runs alongside our API integration and business automation practice, which builds the middleware those agents call.
How an AI development engagement runs
Readiness audit
We map your data sources, access controls, and candidate workflows, then classify each by value and risk. You leave with a concrete integration map — including the honest items where AI is the wrong tool.
Architecture and guardrails
We design the MCP tool surface, retrieval strategy, and permission model, and define the evaluation criteria a release must pass. Security review happens here, not after launch.
Build and evaluate
Senior engineers ship incrementally behind evaluation harnesses. Accuracy, latency, and cost are measured against the agreed budgets before access widens beyond the pilot group.
Operate and extend
Prompts, tools, and models are versioned and monitored in production. As confidence grows, agent capabilities expand — one scoped tool at a time, never by loosening permissions wholesale.
Where this fits
AI integration rarely stands alone. When the LLM feature is one part of a larger platform, it belongs inside a custom software development engagement, where the agent gateway is designed alongside the system it serves. The pipelines, vector stores, and MCP servers themselves need reliable infrastructure — autoscaling, observability, and reproducible environments — which is the territory of our cloud and DevOps practice. Engagements run as an AI readiness audit, a fixed-scope build project, or a dedicated team, depending on how far along your AI roadmap already is.