CustomLLMIntegration
Adding a large language model to an existing product is easy to demo and hard to get right in production. We design the retrieval, prompting, and evaluation layers that make a model integration reliable, auditable, and cost-predictable at scale.
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Wiring an API key into a product takes an afternoon. Making that integration trustworthy enough to put in front of paying customers takes real engineering: grounding the model in your actual data, constraining what it's allowed to say, and measuring whether it's getting better or worse over time. That's the gap we close.
Retrieval-augmented generation is the backbone of most of our integrations. We build the ingestion pipeline, chunking strategy, and vector search layer that let a model answer from your documents, your policies, or your product data instead of its general training knowledge, then tune retrieval quality until it's actually reliable.
We treat prompts as versioned, tested software artifacts, not one-off strings. Every integration ships with an evaluation suite built from real and adversarial examples, so a prompt change or model upgrade is measured against regression before it reaches production, the same discipline you'd expect from any other code change.
We're deliberately model-agnostic. Clients like Lexicore Legal and Havenstay Hospitality have different constraints around cost, latency, and data residency, so we design an abstraction layer that lets you swap between GPT-, Claude-, and Gemini-class models as pricing and capability shift, without a rewrite.
What'sincluded
Retrieval-Augmented Generation
Document ingestion, chunking, and vector search pipelines that ground model responses in your actual data instead of general training knowledge.
Prompt Engineering & Versioning
Prompts managed as tested, version-controlled artifacts with clear rollback paths, not scattered strings buried in application code.
Evaluation Pipelines
Automated eval suites built from real and adversarial examples that catch quality regressions before a prompt or model change ships.
Guardrails & Safety Layers
Input and output filtering, PII detection, and scope constraints so the model stays inside the boundaries your product and compliance team require.
Multi-Model Abstraction
A provider-agnostic integration layer that lets you switch or mix GPT-, Claude-, and Gemini-class models as cost and capability evolve.
Engagementprocess
Use Case & Data Audit
We map exactly what the model needs to know, where that data lives today, and how fresh it needs to be to stay accurate.
Retrieval & Prompt Design
We build the ingestion pipeline and draft initial prompts, then stress-test both against a first batch of real examples.
Evaluation Suite Build
A structured eval set, including edge cases and adversarial inputs, becomes the standard the integration has to clear before launch.
Integration & Guardrails
We wire the model into your existing product, add safety and monitoring layers, and tune latency and cost.
Launch & Continuous Tuning
Post-launch, we track real usage against the eval suite and keep refining prompts and retrieval as usage patterns emerge.
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Relatedcasestudies
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Commonquestions
Primarily through retrieval: the model answers from documents we ground it in rather than open-ended recall, and we constrain prompts to say it doesn't know rather than guess. Our evaluation suite specifically tracks unsupported claims so we can measure improvement rather than assume it.