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Generative AI & LLMs

Design, build, and deploy production-grade LLM applications — grounded, governed, and built for real users.

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From prompt to product

How generation actually works.

A prompt becomes context, runs through a model, and streams back a grounded answer. Tap a stage.

PromptModelOutputSTREAMING OUTPUT
Prompt — Structured prompting, context windows, and tool-use design tuned to your domain.
Overview

Generative AI & LLM development.

We design and build custom LLM-powered applications end to end — from prompt architecture and orchestration to evaluation harnesses and production deployment. Our work spans chat assistants, internal copilots, document intelligence, and customer-facing generative experiences.

We're platform-agnostic — OpenAI, Anthropic, Llama, Mistral, open-weight — and choose the stack that fits your latency, cost, data-residency, and compliance needs, not the other way around.

What's included

Prompt & context architecture

Structured prompting, context windows, and tool-use design tuned to your domain.

Orchestration & tool use

Reliable chaining, function calling, and routing across one or many models.

Evaluation harnesses

Automated evals so quality is measured, not guessed — before and after launch.

Production deployment

Streaming UX, scaling, and cost controls for a real, reliable product.

What we build

LLM applications that ship.

From internal copilots to customer-facing experiences — grounded and governed.

01

Chat assistants & copilots

Domain assistants and internal copilots that actually know your business.

02

Document intelligence

Extraction, classification, and Q&A over contracts, tickets, and knowledge bases.

03

Customer-facing generative UX

Generative experiences your users trust — fast, on-brand, and safe.

04

Structured extraction & summarization

Turning messy text into reliable, structured, downstream-ready data.

05

Fine-tuned & domain models

Adapting models to your domain when prompting and RAG aren't enough.

06

Evaluation & guardrails

Eval suites and output controls that keep quality and safety measurable.

Built for real users

Grounded, governed, and right-sized.

Demos are easy; dependable products are the hard part — that's where we focus.

Grounded & accurate

RAG and retrieval keep answers tied to your authoritative data, cutting hallucinations.

Cost & latency tuned

Model routing, caching, and right-sizing so it's fast and affordable at scale.

Data-residency & compliance

Open-weight and private deployment options when data can't leave your walls.

Human-in-the-loop

Review and approval where the stakes are high — autonomy where they're not.

Startup to enterprise scale

Lean MVPs for founders, hardened platforms for enterprises — same team.

Measurable quality

Evals and monitoring so you can prove the system is good, not just hope it is.

GenAI capabilities

The depth behind every LLM build.

Modern prompt, orchestration, and evaluation engineering.

Prompt engineering
Context architecture
Orchestration
Tool use / function calling
Fine-tuning
Evaluation (evals)
Guardrails
Streaming UX
Multimodal
Cost optimization
RAG integration
Multi-model routing
Modern GenAI stack

Tools & technologies we build with

The right models and frameworks for your constraints — never one-size-fits-all.

Models
OpenAI
Anthropic
Llama
Mistral
Frameworks
LangChain
LlamaIndex
DSPy
Serving
vLLM
TGI
Bedrock
Vertex AI
Eval & Guardrails
Ragas
promptfoo
LLM Guard
Data
pgvector
Pinecone
Weaviate
Cloud
AWS
Azure
GCP
Our approach

How we deliver generative AI

1

Frame use case

Define the job, users, and what 'good' means in measurable terms.

2

Prompt & context design

Architect prompting, context, and tool use for your domain.

3

Build & integrate

Wire the app into your data and systems with reliable orchestration.

4

Evaluate & harden

Run evals, add guardrails, and tune cost and latency.

5

Deploy & monitor

Ship with streaming UX and keep quality and spend under watch.

200+
Projects delivered
50+
Worldwide clients
120+
Skilled experts
2017
Building production AI
FAQ

Common questions

How long until we have something working?+
Often a useful prototype in weeks. We move fast to validate value, then harden for production.
How do you stop the model from hallucinating?+
Grounding with RAG, output validation, guardrails, and evaluation — so answers stay tied to your real data.
Which model should we use?+
Whichever fits your latency, cost, data-residency, and quality needs. We're platform-agnostic and will benchmark options for you.
How do you control cost?+
Model routing, caching, right-sizing, and evals that catch regressions before they get expensive.

Have an LLM idea?

Consultation is free. Tell us what you want it to do — we'll build it grounded and governed.

Discuss your project