Proposals and RFP responses
First drafts built from your past proposals, service descriptions and price books, with gaps flagged for your team to complete.
PRODEV Info Solutions designs, deploys and runs generative AI agents that produce real business work: proposals, reports, knowledge answers, marketing content and code. They work from your data and brand rules, and every important output goes through your approval steps.
Generative Agent as a Service (GaaS) is a managed service in which a provider builds, hosts and continually improves AI agents whose main job is to create: documents, analysis, content, images or code. The agent works from your approved knowledge and follows your rules, and your people review what it produces before it is used.
You get a working capability and a team accountable for it, without having to build or run the underlying AI stack yourself.
GaaS is an emerging label rather than a formal industry standard, and the same letters are used elsewhere for unrelated services such as gaming as a service. On this page it has only the meaning above.
A generative agent is most useful where skilled people spend hours assembling a first version from information your business already has.
First drafts built from your past proposals, service descriptions and price books, with gaps flagged for your team to complete.
Recurring reports and executive summaries drawn from spreadsheets, tickets and meeting notes, in the format you already use.
Briefs, drafts and localised versions, including Indian languages, that follow your brand voice and go to an editor before they are published.
Answers for staff or customers that come with links to the source documents, using retrieval-augmented generation (RAG).
Code scaffolds, test cases, API documentation and release notes, proposed through your existing review process.
Suggested replies, follow-up emails and message templates in your tone, ready for a staff member to check and send.
You work with one team across the whole lifecycle, from choosing the first use case to improving the agent in production.
We find where a generative agent will save real time and agree how success will be measured.
We connect the approved sources and carry your access rules through to what the agent can read.
We write the instructions, brand rules, output formats and refusal behaviour, and choose the right model for each job.
We test the agent against examples from your own work, then re-test whenever a model or prompt changes.
We put approval steps where your team already works, so checking an output is quick and recorded.
We track quality, usage and cost, and use your reviewers' corrections to improve the agent.
All three can be the right choice. The difference is who carries the work of making the AI reliable in your business.
| General AI chat tool | In-house build | GaaS | |
|---|---|---|---|
| Who builds and maintains it | The vendor builds the tool. Your staff work out how to use it. | Your own engineering team | PRODEV, with your team setting the requirements |
| Knowledge of your business | Whatever each user pastes or connects | As much as your team integrates | Connected to your approved sources, with your access rules |
| Quality control | Left to each individual user | Whatever your team builds | Agreed test sets, review steps and ongoing monitoring |
| Consistency across the team | Varies from person to person | High, once it has been built | High: shared instructions, formats and brand rules |
| Effort to start | Very low | High: specialist hiring and platform work | Moderate: discovery and a focused pilot |
| Typical cost shape | Per-seat subscription | Salaries and infrastructure | Setup, a monthly service fee and model usage |
Five stages take the agent from an idea to a monitored production service. It earns wider use by passing each one.
We map the work, pick one high-value use case and agree what a good output looks like.
We prepare the source material and connect it securely, with your access permissions applied.
We design the agent, its output formats and its approval steps around how your team works.
We score it against real examples, then run a small pilot in which your people check every output.
We go live with monitoring and cost controls, then review results with you and improve the agent.
Generative models can be confidently wrong, so we design every agent on the assumption that it sometimes will be.
Important outputs are reviewed by a person before anyone relies on them.
Knowledge answers link back to the documents they were based on, so they are quick to check.
The agent can read only what its user or role is permitted to see.
Before go-live we document which AI providers process your data, where, and under what terms.
Test sets run again whenever a model or prompt changes, so quality problems are caught early.
Inputs, sources, outputs and approvals are logged so decisions can be reviewed later.
We use the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework as design references. They are public guidance that we follow, not certifications that we hold.
Last updated 6 November 2026. Also see our AI Solutions and Data Science services.
Describe one document, report or answer your team produces repeatedly. We will tell you honestly whether a generative agent is a good fit and what a sensible first pilot would look like.
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