Generative AI

Generative AI that ships work your team signs off.

The gap between a generative AI demo and a generative AI system is everything around the generation: grounding in your data, your brand's voice, review workflows, and a definition of "good" someone actually measured. We build the whole system, not the demo.

Talk to us All AI development

Draft generator grounded · on-brand

PromptDraft the Q3 renewal proposal for Acme Ltd

renewal-proposal-acme.md

Pricing from CRM Last year’s terms Brand voice

Draft ready for review · 40 min → 90 sec

In one paragraph

AGI Software Solutions is a generative AI development company building custom GenAI systems — document and proposal drafting, content pipelines, copilots and multimodal generation — grounded in your own data, matched to your brand voice, and wrapped in the review workflows that make AI-generated work safe to ship. Every system leaves with evals attached, so quality is measured rather than assumed.

What We Build

Generation is easy. Shippable is the work.

Any model can produce a plausible draft. A production system produces drafts from your data, in your voice, with a human review step sized to the risk — and gets measurably better as your team corrects it.

Grounded

Generated from your data

Drafts assembled from your CRM, documents and history — so the proposal quotes the right pricing and the report cites real numbers.

On-brand

Your voice, enforced

Style, terminology and structure encoded into the system rather than re-explained in every prompt.

Reviewed

Humans at the right point

Review steps sized to risk: light-touch for internal drafts, mandatory sign-off for anything that leaves the building.

Measured

Quality with a number on it

Output evals and acceptance-rate tracking, so "is it good?" has an answer that is not an opinion.

Plain English

What are custom generative AI solutions?

Custom generative AI solutions are systems that produce work product — documents, reports, content, designs, code — using large language and multimodal models, but built around one organisation: grounded in its data, tuned to its voice and standards, integrated into its tools, and gated by its review process. The distinction from consumer AI tools is that the output starts from your context, not from a blank prompt.

Compared

Custom GenAI vs off-the-shelf AI tools

ChatGPT is excellent. It is also generic, and quality depends on whoever wrote the prompt that day.

	Consumer AI tools	Custom generative AI system
Starting context	A blank prompt, every time	Your data, pulled in automatically
Output consistency	Depends on the prompter	Templates, voice and rules built in
Review workflow	Copy-paste and hope	Draft → review → publish, tracked
Quality over time	Static	Improves from corrections and evals
Right choice when	Individual productivity	A recurring output the business depends on

How We Build It

Five stages from problem to production.

The same process across every AI Development project, scaled to the size of the problem.

Discovery

We work out what the system actually has to do, what data exists, and what happens today when it goes wrong.

Design

Architecture, model choice, integration points and failure handling, defined before any of it gets built.

Integration

Connecting to the systems that hold your data, with security and permission boundaries handled properly.

Automation

The system takes on real work, in the workflows your team already uses rather than beside them.

Refine

Tuned against real usage and measured with evals, because how people use a system is never quite how it was designed.

Use Cases

Where generative ai pays off.

The patterns we see deliver, across startups, SMEs and enterprise teams.

Sales

Proposals and renewals

First drafts assembled from CRM data, past terms and your templates — 40 minutes of assembly work down to a 90-second review.

Content

Content pipelines

Product descriptions, campaign variants and localised copy generated at volume, inside brand rules, with human sign-off where it counts.

Reporting

Narrative reporting

The written commentary around your numbers — board packs, client reports, summaries — drafted from the data itself.

Engineering

Internal copilots

Assistants that draft in your team's context: support replies, specs, documentation — grounded in how your organisation actually writes them.

Related Work

Systems we have shipped.

AI DevelopmentAI Chatbots

Conversational AI Studio

Context-aware multi-channel chat that qualifies inbound leads and routes them to the right person.

3.2× qualified leads, 54% faster replies

AI DevelopmentMarketplace

AI Marketplace Platform

Support chatbot, live SEO analyser and a vector recommendation engine, all sharing one retrieval layer.

+10% GMV, 40% fewer support tickets

AI DevelopmentComputer Vision

Smart Video Chunker

A pipeline that chunks long-form video into searchable scenes, transcripts and entity timelines.

Under 12ms inference at 30 FPS

See all case studies

What Teams Say

Hear from the teams we work with.

“We needed a voice agent that could actually qualify leads, not a chatbot pretending to be one. The team shipped a sub-700ms pipeline in 6 weeks. It now handles 5k calls a day.”

Priya RajHead of Growth, Ninjatech

“What sold us was their willingness to put AI engineers and product designers on the same call. We got working prototypes by week two and a production rollout in three months.”

James ThorntonCTO, Allindex

“AGI designed a CRM system tailored to our client management process. It is intuitive, reliable, and has centralized all our communication and history in one dashboard. This has greatly improved client retention.”

Carlos MendesProduct Manager, Qilinlab

Common Questions

Before you get in touch.

What can generative AI actually automate for a business?

Recurring written and visual output with a clear pattern: proposals, reports, product content, support replies, documentation. The best candidates are high-volume, template-shaped and currently eating skilled people's time. One-off creative work is a much weaker case.

Will it hallucinate things into our documents?

Ungrounded generation will. That is why our systems draft from retrieved facts — your data, your documents — and why outputs that leave the building pass a human review step. Grounding plus review is the honest answer; no vendor can promise zero.

What does generative AI development cost?

A focused generation workflow (one output type, one data source) typically runs $20k–$50k. Multi-source systems with review workflows and brand controls run $50k–$150k. Ongoing cost is mostly model usage, engineered down with caching and routing.

Is our data used to train anything?

No. Your content grounds generation at request time and is never used to train external models. Where even API access is too much exposure, we deploy open-weight models inside your network.

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