Which models do you use for generation?

We are model-agnostic and often combine frontier and open models — routing each request to the cheapest model that clears the quality bar for that task.

How do you prevent hallucinations and unsafe outputs?

Retrieval grounding, structured outputs with schema validation, policy filters and human review on high-stakes flows, all backed by continuous evaluation.

How do you control inference cost at scale?

Model routing so each request goes to the cheapest model that clears the quality bar, plus prompt caching, batching, and distilling frequent tasks into smaller models. We measure cost per request from the start so the effect is visible rather than assumed.

Generative AI

Generative systems that create on brand, and on spec

We build generative AI that produces content, code, imagery and structured data your teams can actually ship — grounded in your voice, bounded by your policies and priced for production.

Get started What we build

Technology we build with

PythonFastAPILangChainLangGraphAnthropic ClaudeOpenAIGoogle GeminiPostgreSQLpgvectorDockerAWSNext.js

Overview

From a clever demo to a dependable creative engine

Most generative pilots stall because tone, accuracy and cost stop holding up at volume. We close that gap with retrieval grounding, deliberate prompt and model architecture, and an evaluation harness that holds every output to a measurable bar before it reaches a customer.

Brand-grounded generation tuned to your voice, style guide and terminology
Structured outputs with schemas and validation, not free-text you have to clean up
Safety guardrails, PII redaction and human-in-the-loop review where stakes are high
Cost controls via model routing, caching and distillation to keep unit economics sane

agent.py

1from langgraph.graph import StateGraph
2from tools import search_docs, lookup_order
3 
4graph = StateGraph(SupportState)
5graph.add_node("retrieve", search_docs)
6graph.add_node("act", lookup_order)
7graph.add_node("review", human_approval)
8 
9agent = graph.compile()
10result = agent.invoke(ticket)  # traced end to end

What we build

Generative capability, engineered end to end

A complete generative stack — from grounding and orchestration to guardrails and evaluation — delivered to your cloud.

Multimodal generation

Text, code, images and synthetic data from a single orchestrated pipeline with consistent quality controls.

Brand-safe guardrails

Policy filters, tone enforcement and forbidden-topic controls that keep every output on-message.

Grounded content engine

Retrieval over your approved sources so generated copy stays accurate, current and citable.

Code generation

Assistants that scaffold, refactor and document code against your repos, conventions and CI.

Evaluation harness

Automated scoring for factuality, tone and safety that gates releases the way tests gate software.

Cost-tuned inference

Model routing, caching and distillation that cut spend without giving up on output quality.

Capabilities

One platform for every generative workload

The building blocks we assemble into your production generative system.

01Marketing & product copy

02Personalized customer communications

03Synthetic data for training & testing

04Image & creative asset generation

05Code scaffolding & documentation

06Long-form report & summary drafting

07Multilingual content localization

08Prompt libraries & template governance

09A/B evaluation of generated variants

Evaluation-driven

Every build ships with an evaluation suite, so quality is measured rather than asserted.

Deployed your way

Your cloud account, VPC or on-premise — including open-weight models where data cannot leave.

Source-code handover

You receive the code and the documentation. No lock-in to us to keep it running.

Human in the loop

Approval gates and review queues wherever an automated mistake would be costly.

FAQ

Common questions

We ground generation in your approved sources, enforce your style guide through prompts and guardrails, and run every output through an evaluation harness that scores tone, factuality and safety before it ships.

Keep exploring

Explore more

AI Agents & Agentic Automation

Systems that plan and act

AI Knowledge Assistants

Answers from your own documents

LLM Customisation

Fine-tuning & domain adaptation

AI Development

End-to-end delivery

Put generative AI to work this quarter

Start with one high-value content or code workflow and see a production-ready system in weeks.

Start a conversation How we work