Should we use a hosted API like OpenAI or self-host an open model?

It depends on data sensitivity, cost at your expected volume, and latency requirements. Hosted APIs are usually faster to get to production; self-hosted open models make sense when data governance rules out third-party APIs or when volume makes hosted pricing uneconomical. We model both against your actual constraints before recommending one.

How do you prevent hallucinated or incorrect outputs?

Grounding responses in retrieved, verifiable source data through a well-tuned RAG pipeline is the primary lever, combined with output validation and guardrails that catch responses falling outside expected patterns. We also build an evaluation framework so regressions are caught before reaching users, rather than relying on the model 'just being good.'

What if our proof-of-concept was built by another team or vendor?

We can assess an existing proof-of-concept, identify why it isn't production-ready, and either extend it or rebuild the parts that don't hold up under real evaluation. This is common: PoCs are frequently built to look good in a demo, not to survive production data and edge cases.

How do you control ongoing inference costs?

Cost controls include caching repeated queries, retrieval tuning to reduce prompt size, model routing (cheaper models for simpler queries), and monitoring that flags cost anomalies early. We build these in from the start rather than retrofitting them after a surprising bill.

Solution

AI Transformation & Automation

Leadership wants an AI strategy, a proof-of-concept impressed everyone in a demo, but nobody has a credible path from that demo to something running reliably in production against real data and real users.

Approach

How we tackle it

We treat the model as one component in a larger system, not the whole solution. Most production AI failures trace back to weak retrieval and grounding, not the model itself, so we invest early in data pipeline quality: chunking strategy, embedding choice, and retrieval evaluation, before optimizing prompts. Every use case gets an explicit evaluation framework with real test cases from your domain, because 'it looked good in the demo' is not an engineering acceptance criterion.

Current-state pain points

Proof-of-concept works in a demo but fails on real-world edge cases
No evaluation framework to know if the model is actually improving or regressing
Sensitive data can't be sent to third-party APIs without a governance review
No infrastructure for retrieval, embeddings, or grounding responses in company data
Cost of LLM API calls scales unpredictably as usage grows
No clear owner for monitoring model behavior in production

What is in scope

Use case scoping and technical feasibility assessment
RAG (retrieval-augmented generation) pipeline design over proprietary data
Model selection and evaluation framework (hosted API vs. self-hosted/open models)
Prompt engineering, guardrails, and output validation
Integration with existing workflows, data stores, and applications
Production deployment: latency, cost controls, monitoring, and human-in-the-loop fallback

Who it is for

Companies with a promising AI proof-of-concept stuck in demo purgatory
Organizations wanting to automate manual, judgment-heavy workflows with LLMs
Teams building internal knowledge or support tools on top of proprietary data
Engineering leaders needing an honest technical assessment of what AI can and can't do for a given use case

Prerequisites

A specific, scoped use case rather than an open-ended 'add AI' mandate
Access to the underlying data the AI system needs to reason over
A stakeholder who can define what 'correct' output looks like for evaluation
Clarity on data governance constraints for any third-party model APIs

Deliverables

What you end up owning

Artifacts land in your repositories and cloud accounts, with documentation to match.

Feasibility assessment with recommended approach and architecture
RAG pipeline including data ingestion, chunking, embeddings, and retrieval
Evaluation framework with test cases and quality metrics for the specific use case
Guardrails and validation layer for model outputs
Production integration with existing applications, APIs, or workflows
Monitoring and observability for model behavior, latency, and cost
Documentation on model/version decisions and known limitations

Implementation

How the work is sequenced

Feasibility & Scoping

Assess whether the use case is a good fit for current LLM capabilities and define success criteria upfront.

Data & Retrieval Pipeline

Build the data ingestion, chunking, and retrieval layer that grounds model outputs in your actual data.

Model Integration & Evaluation

Select and integrate the model, build an evaluation framework with real test cases, and iterate against measured quality.

Guardrails & Integration

Add output validation, guardrails, and integrate the pipeline into existing applications or workflows.

Production Hardening

Add monitoring, cost controls, and fallback paths, then hand over documentation on limitations and operating procedures.

Technologies

What we typically use

Models & APIs

OpenAIAnthropicOpen-weight models (Llama, Mistral)

RAG & Retrieval

LangChain / LlamaIndexVector databases (pgvector, Pinecone, Weaviate)Hybrid search

Serving & Infra

vLLMKubernetes GPU node poolsCloud-managed inference endpoints

Evaluation & Observability

Custom eval harnessesLangSmith / ArizeOpenTelemetry tracing

Outcomes

What changes when this is done

Qualitative outcomes only. Any figures depend entirely on your estate, and we will not quote them before measuring.

AI functionality validated against real evaluation criteria, not demo impressions
Model outputs grounded in proprietary data rather than hallucinated
A clear architectural boundary between model, retrieval, and application logic
Predictable cost and latency behavior in production
A monitoring setup that surfaces model quality regressions before users report them

FAQ

Common questions

Related service

Artificial Intelligence

Talk to the engineers who would do the work

Bring your current architecture, constraints and the problem you are trying to solve. We will tell you what we would change first, what it depends on, and where we would start.

Book a Technical Consultation See how we work