We connect language models to the software and data your company already runs, adding features such as search, summaries, drafting and document reading with controls your team can audit.

Model integrationRetrieval pipelinesEvaluation suites

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Model integration

Retrieval pipelines

01Our Approach

An AI feature is finished when you can measure how often it is right and see what it does when it is not.

02Overview

AI features inside the systems you already run

01Model integration
02Retrieval pipelines
03Evaluation suites

AI integration adds language model capabilities to existing products and internal tools. We work out where a model helps, connect it to your data through retrieval and APIs, build the interface and the review steps, and set up evaluation so quality is measured rather than assumed. The model becomes one component in your system, with logging, limits and fallbacks around it.

It suits teams with a working product or internal platform and a clear task in mind, such as answering questions from documents, drafting replies, extracting fields or routing requests. If you are still exploring what AI could do for the business, a short discovery comes first. We select models for the task, cost and data rules, and avoid tying you to one provider.

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03What We Do

Capabilities, End to End

Use case selection

We assess candidate tasks for value, data availability, error tolerance and cost, and recommend where a model is useful and where ordinary software is the better answer.

Retrieval and context

Your documents and records are indexed with embeddings and metadata, so the model answers from current company information and cites the sources it used.

Tool and API access

Models call defined functions in your systems to look up records or prepare actions, within permissions that match the signed-in user.

Review and approval

Outputs that affect customers, money or records pass through a person, with the model's evidence shown beside its suggestion and every decision logged.

Evaluation and testing

A test set built from your real cases scores accuracy, tone and safety on every change, so prompts and models are compared with evidence.

Cost and performance

Caching, model routing, streaming and usage limits keep response times and spending predictable as more people across the company use the feature.

04In Production

What It Looks Like When It Ships

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Master service agreement

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Model integration

Retrieval pipelines

Evaluation suites

Cited answersHuman reviewQuality scoresUsage limits

05What You Receive

Handed Over, Ready to Run

SCROLL

Use case assessment

AI feature in production

Retrieval index of your content

Evaluation set and scores

Prompt and model configuration

Review and approval screens

Usage and cost dashboard

Documentation and source code

06How It Runs

From Kickoff to Launch

Select and scope

Prototype with data

Integrate and guard

Pilot and tune

Select and scope

We examine the task, the data it needs and the cost of a wrong answer, then define one feature narrowly enough to build and measure, with success criteria agreed in advance.

Prototype with data

A working prototype runs on a sample of your documents or records. We build the first evaluation set from real cases and learn where the model fails before design is finalized.

Integrate and guard

The feature is built into your product with permissions, logging, validation, rate limits and fallbacks, and with review steps added wherever an error would matter to customers or records.

Pilot and tune

A limited group uses it on live work. We track accuracy, corrections, speed and cost, improve prompts and retrieval, and widen access when the scores hold.

07Technology

The Stack We Use

ModelsOpenAI APIAnthropic APIGoogle GeminiAmazon BedrockOrchestrationLangChainLangGraphVercel AI SDKTemporalModelsOpenAI APIAnthropic APIGoogle GeminiAmazon BedrockOrchestrationLangChainLangGraphVercel AI SDKTemporal

RetrievalpgvectorPineconeElasticsearchLlamaIndexQualityLangfuseLangSmithPromptfooOpenTelemetryRetrievalpgvectorPineconeElasticsearchLlamaIndexQualityLangfuseLangSmithPromptfooOpenTelemetry

08Who It Is For

Built for Teams Like Yours

FLAGSHIP

You know the task and need it in production

Your team has tried a chatbot or a prototype and seen promise, yet nothing is connected to real data or trusted enough to release. You need the feature inside your existing software, answering from your information, with permissions, review steps and measurements you can show to management.

Knowledge search across documents

Support reply drafting

Document data extraction

Request triage and routing

Report and summary generation

Assistants inside internal tools

09Questions

What buyers ask before they start

If yours is not here, ask us directly. The first conversation is free and useful whether or not we work together.

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Integration adds capabilities to software you already have, such as a search box that answers from your documents or a draft button in your support tool. An AI product is a new application built around the model. Integration is usually faster to deliver and easier to adopt because people keep working in the tools they know.

It does not have to be. We use provider APIs and enterprise agreements under which your inputs are not used for training, and we can deploy through your own cloud account with services such as Amazon Bedrock or Azure OpenAI. Sensitive fields can be masked before they reach a model. We document the data flow so your security team can review it.

We design for them. Answers are grounded in retrieved sources and shown with citations, outputs are validated against rules, and the model is instructed to say when it lacks information. An evaluation set measures error rates before release. For actions that matter, a person approves the result. Users can flag mistakes, and those cases join the test set.

It depends on the task, the languages involved, response time, cost and where data may be processed. We test several models on your own cases instead of choosing by reputation, and build the integration so the model can be replaced without rewriting the feature. Many systems use a smaller, cheaper model for routine steps and a stronger one for difficult cases.

Yes. We begin by reading the codebase and its APIs, then agree with your developers where the feature lives and how it is deployed. Often we deliver the AI capability as a separate service with a clear interface, which keeps changes to the existing application small. Your team reviews our work through pull requests in your own repositories.

GuidesFree to use with any company