Teams shipping their first production LLM feature
Where you have a clear use case but need engineers who have built evaluation, guardrails, and observability for live AI before.
Applied AI
Production AI that solves a specific problem.
LLM applications, retrieval-augmented generation, agents, and forecasting — built with evaluation, guardrails, and observability from day one. No slideware demos.
The problem
Most AI proofs of concept never reach a user.
A demo on a curated dataset is easy. A system that handles real inputs, fails gracefully, stays within budget, and gets better over time is hard — and that is where most AI engagements stop. We focus on the production part: evaluation harnesses, retrieval quality, guardrails, fallbacks, and the operational habits that keep an AI system trustworthy six months in.
What an engagement looks like.
- 01
Use-case scoping
Two-week sprint to test whether AI is actually the right tool for the problem. If a SQL query or a rules engine wins, we will say so — and you save the budget.
- 02
Prototype with real data
A working prototype on a representative slice of your data, with an evaluation harness that measures the metric that actually matters to your users.
- 03
Production hardening
Retrieval, guardrails, prompt versioning, observability, cost monitoring, and the unglamorous failure modes — caching, fallbacks, rate limits, PII redaction.
- 04
Run and iterate
We monitor real usage, fix what regresses, and keep evaluations green as models and prompts evolve. AI systems decay without a maintenance habit.
What you get
Concrete deliverables, not just hours.
- Use-case scoping report with a clear go / no-go recommendation
- Working prototype with reproducible evaluation harness
- Retrieval pipeline (chunking, embeddings, vector store, reranking)
- Production-grade LLM application with prompt versioning
- Guardrails: input validation, output filters, PII redaction, jailbreak defences
- Observability: traces, prompt logs, eval scores, cost per request
- Fallback paths for model outages, rate limits, and degraded responses
- Runbook and team enablement for ongoing maintenance
Where we add the most value.
Replacing a manual workflow with an AI-assisted one
Customer support triage, document extraction, content review — places where the metric is hours saved, not vibes.
Decision and forecasting systems
Demand forecasting, pricing, fraud signals, recommendation — classical ML and modern LLM techniques applied where each fits.
FAQ
Applied AI — common questions
What kinds of AI projects does DigiHawks take on?
Mostly production LLM applications (chat assistants, document Q&A, agentic workflows), retrieval-augmented generation pipelines, and forecasting or decision systems. We are pragmatic: if a use case is better served by a rules engine or a small classical model, we will recommend that instead.
Which models and providers do you work with?
We build provider-agnostic pipelines and choose the right model per task — OpenAI, Anthropic, Google, open-source models on AWS Bedrock or Azure AI, and self-hosted options where data sensitivity or cost demands it. Provider lock-in is a risk we actively design around.
How do you evaluate AI systems?
Every project ships with an evaluation harness — a set of representative inputs scored on the metric that matters (accuracy, helpfulness, latency, cost). We re-run evaluations on every prompt or model change so regressions are caught before users see them.
How do you handle data privacy with LLMs?
We never train on customer data without explicit consent and a contract. For sensitive workloads we route through providers with no-retention agreements, deploy in your cloud account, or use self-hosted models. PII redaction and audit logging are standard, not extras.
How much does an AI engagement cost?
Scoping sprints are fixed-fee (typically two weeks). Prototype builds run four to eight weeks. Production engagements are time-and-materials with a capped monthly burn. We send a written proposal after a short discovery call.
Have a use case in mind?
Tell us what you are trying to automate or improve. We will tell you whether AI is the right hammer.