Selected productization cases

A few examples of how we turned fragile internal tools into solid products. Details are anonymized, but the patterns are real.

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Case 1Coordinated Vendor Q&A Platform Case 2Email-native reporting Case 3AI RFP Builder

Real products start with real problems

Here are a few anonymized cases where clients hit a brick wall with their internal tools and we helped turn those constraints into scalable, product‑grade solutions.

Case: Vendors Q&A

A procurement firm needed to tame chaotic, call-heavy vendor evaluations. We built a collaborative Q&A and evaluation platform that keeps everything written, comparable, and transparent across departments and vendors.

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Step 01
Context & starting point
- Mid-sized procurement/sourcing firm specializing in helping enterprises evaluate and select vendors.
- They coordinated multi-vendor evaluations where deal sizes were large and multiple internal departments (sales, marketing, etc.) had different information needs.
- A recurring problem was vendors insisting on “jumping on a call” for every clarification, turning routine Q&A into high-pressure sales conversations.
Step 02
The internal tool (as we found it)
- Process centered on manually created forms (documents or basic form tools) emailed to vendors.
- One coordinator collected questions from all departments, updated a master form, and notified vendors of new or revised questions.
- Vendors often pushed back to calls but eventually filled the forms, triggering another round of back-and-forth when departments had follow-up questions.
- No shared, real-time view: departments had limited visibility into each other’s questions, vendor responses, or evaluation status.
- Evaluation was done via large end-of-process meetings where teams manually tallied feedback and discovered missing information late.
Step 03
Why change / trigger
- High time cost and coordination overhead for the procurement team managing multi-vendor, multi-department Q&A by email and static forms.
- Vendors repeatedly steering communication to sales calls, undermining the client’s need for written, comparable answers.
- “Broken telephone” between departments: delayed follow-up questions, conflicting asks, and last-minute discovery of gaps.
- Existing form-builder apps still required a central operator, did not support dynamic departmental input, and were not designed for iterative vendor Q&A.
Step 04
What we did
- Analyzed the end-to-end vendor Q&A workflow: question collection from departments, form creation, vendor responses, and evaluation.
- Identified key requirements:
  - Distributed question authoring by multiple departments.
  - Single consolidated vendor form that updates in real time.
  - Versioning and change notification for vendors.
  - Per-question ownership and per-answer evaluation.
- Designed a multi-tenant web application that:
  - Lets each internal department log in and create, edit, and tag their own questions.
  - Automatically merges all department questions into a single structured vendor questionnaire per opportunity.
  - Tracks versions of the questionnaire and sends automatic notifications to vendors when new questions are added.
- Implemented UX patterns to reduce friction and push vendors to written responses:
  - Clear deadlines and instructions at the top of each vendor form.
  - Persisted state: previously answered questions remain collapsed but accessible; new/changed questions are surfaced prominently.
- Built notification and review workflows:
  - When a vendor answers a question, the originating department is immediately notified.
  - Departments can rate, comment on, and flag responses as they arrive, instead of waiting for a final review phase.
- Exposed transparent status views:
  - Who asked each question.
  - Which vendors have answered which questions.
  - How each answer has been rated by the relevant stakeholders.
Step 05
The new product
- A collaborative vendor Q&A and evaluation platform used by procurement teams, internal departments, and external vendors, now also repurposed as a core client-onboarding tool.
- Primary users: procurement leads (set up opportunities and vendors), functional departments (create questions and review answers), external vendors (respond to questionnaires), and new client organizations during onboarding.
- Main capabilities:
  - Multi-department question authoring and editing, combined into a unified external-facing form.
  - Real-time updates and versioning with automatic notifications.
  - Vendor/client view optimized for incremental completion, highlighting only new/unanswered questions.
  - Per-question ownership, instant departmental notifications on responses, and in-line rating/review.
  - Full transparency on question coverage and response status across all stakeholders, for both vendor evaluations and new client onboarding.
Step 06
Results & impact
- Dramatically reduced need for vendor “jump on a call” sessions by establishing a clear, structured written Q&A process.
- Removed the coordinator bottleneck: departments now ask and review their own questions directly in the system.
- Shortened evaluation cycles by enabling continuous, asynchronous review instead of large end-of-process meetings.
- Increased transparency and auditability: every question, answer, and rating is traceable to a department and vendor/client.
- Improved comparability of vendors’ responses, enabling more objective scoring and better decision support.
- The platform evolved into a key component of their commercial offering, used to onboard new clients and demonstrate a differentiated, structured procurement process.
Step 07
Tech highlights
- Web-based, multi-user application with role-based access for internal departments and external vendors/clients.
- Real-time form aggregation and version tracking to manage evolving question sets per opportunity or onboarding engagement.
- Notification system integrated with email for external alerts and departmental response notifications.
- Data model designed around questions, owners, organizations (vendors/clients), responses, and ratings to support transparent evaluation and onboarding workflows.
Step 08
Where we left them
- The procurement firm now owns and operates the platform as its standard tool for vendor evaluations and new-client onboarding, with ongoing internal enhancements.
- It is actively used across engagements and packaged as a central part of their value proposition to new and existing clients.

Email-native reporting for VFX leadership

A global VFX and animation studio relied on a fragile internal reporting tool for mission-critical production and resourcing decisions. We turned leadership's natural email workflows into a robust, AI-powered reporting interface.

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Step 01
Context & starting point
- Global VFX / animation studio with multiple large, concurrent productions and several management layers across many studios.
- A central reporting system aggregated production data (shots, artists, tasks, time, etc.) to support daily and strategic decisions.
- All major operational and resourcing decisions depended on these reports being accurate and timely.
Step 02
The internal tool (as we found it)
- Backend built from ad‑hoc Python scripts orchestrated by a centralized web application running on the internal network.
- UI was a parameter‑heavy web form that generated SQL‑like queries for production, project, and artist data.
- No robust input validation: incorrect date formats, project/artist identifiers, or ranges would generate invalid queries or runaway jobs.
- A single bad query could freeze report generation for everyone or crash the report service, often requiring manual restarts.
- Most managers bypassed the UI and asked the technical director (TD) to run reports manually, creating a severe bottleneck and key person risk.
- Multiple internal rewrite attempts by pipeline/dev tools engineers improved code quality but not UX; the tool remained “usable only by the TD.”
Step 03
Why change / trigger
- The TD and senior developers were spending a disproportionate amount of time generating reports and firefighting issues instead of higher‑value technical work.
- Leadership had lost confidence in further investing in the existing web app due to sunk costs, yet the tool remained mission‑critical.
- On‑prem access (local network / VPN only) meant many executives simply did not use the web app, compounding under‑utilization.
- The organization needed a reporting interface that matched leadership workflows without another large rebuild of the legacy UI.
Step 04
What we did
- Assessed the existing system end‑to‑end: Python scripts, database structure, report types, and the current web UI’s parameter model.
- Shadowed different user groups (TDs, production managers, non‑technical executives) to map how questions about project status were actually asked and answered.
- Concluded that any UI reproducing the full parameter set (even with cascading / tree‑like filters) would remain complex and error‑prone for non‑technical users.
- Identified that the most natural UX already in use by leadership was plain‑language requests (“Can I see artist X’s utilization for Project Y?”) sent via email or verbal asks to the TD.
- Designed a new interaction model where leadership sends plain‑language questions to a dedicated email inbox instead of logging into the internal web app or using VPN.
- Implemented an AI‑driven middleware service that:
  - Monitors the inbox and extracts the user’s intent and requested data fields.
  - Constructs and executes the appropriate database queries against the existing reporting DB.
  - Serializes and prunes raw result sets into a clean, compact representation to avoid confusing the language model.
  - Builds a lightweight RAG (Retrieval‑Augmented Generation) context from the serialized data.
  - Generates structured tables and a concise narrative summary of the results.
  - Drafts and sends a reply email with both tabular data and a human‑readable explanation.
  - Handles follow‑up questions (for example “What about in Q4?”) by reusing prior context and adjusting the underlying query.
Step 05
The new product
- A standardized “reporting via email” AI service that turns natural language questions from leadership into structured production reports.
- Primary users: executive leadership, production managers, and department heads across all studios, who previously depended on the TD to run queries.
- Main capabilities:
  - Free‑form question input via standard corporate email (no VPN or app login required).
  - Automatic query generation and execution on the existing production data store.
  - RAG‑based summarization that returns both a data table and an executive‑level narrative.
  - Support for conversational follow‑ups that refine time ranges, projects, or artists.
- Rolled out as a single internal product used company‑wide across the client’s global studio network.
Step 06
Results & impact
- Near‑instant adoption across leadership in multiple studios; no UX training required as everyone already used email.
- TD and key developers reclaimed significant time previously spent on manual report generation and service restarts.
- Reporting access decoupled from VPN / local network friction, increasing frequency and breadth of data‑driven questions.
- Reduced operational risk by removing the TD as the single gateway to critical reporting, while keeping the existing reporting backend intact.
- Improved quality and consistency of reports, as AI‑generated queries and validation logic replaced brittle manual parameter entry.
Step 07
Tech highlights
- Reused existing Python‑based reporting scripts and database schema as the core data source.
- Added an email ingestion service (mailbox listener) that triggers the AI orchestration workflow.
- Implemented a RAG pipeline: structured query → result serialization → context‑building → LLM summarization and table generation.
- Integrated a modern LLM (vendor‑agnostic here) for intent parsing, query drafting, follow‑up handling, and narrative generation.
- Kept deployment within the studio’s existing infrastructure and security model, while exposing access through standard corporate email.
Step 08
Where we left them
- The product is now owned as a standard internal reporting service by the central pipeline/IT tools team, who maintain the AI service and extend supported question types as new reporting needs emerge.
- It is in active use across all studios by leadership and production, with a roadmap to deepen coverage of additional production metrics and scenarios.

AI-assisted RFP creation and collaboration platform

A software vendor selling into enterprise RFPs wanted to turn chaotic, document-based requirements into a structured, AI-assisted RFP creation product they could use internally and sell to customers.

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Step 01
Context & starting point
- The client is a software vendor that sells primarily through RFP-driven enterprise deals.
- Every prospect ran RFPs differently (Excel, Word, ad hoc templates), creating inconsistent, hard-to-navigate requirements sets.
- RFPs often spanned hundreds of pages, with critical items like compliance scattered throughout, making them slow and error-prone to respond to and hard for buyers to manage.
Step 02
The internal tool (as we found it)
- Initially, there was no unified tool—RFPs arrived as unmanaged documents from prospects.
- Requirements from multiple buyer departments (IT, marketing, compliance, etc.) were mixed across large documents with no shared structure.
- Versioning was manual: edits and new requirements were tracked in document history or email threads.
- There was no systematic way to reuse past RFP structures or templates by industry or deal type.
Step 03
Why change / trigger
- The vendor saw a recurring pattern: every customer was reinventing the RFP process in a slightly different, inefficient way.
- This created friction on both sides—clients struggled to assemble coherent RFPs, and the vendor spent time parsing messy documents.
- The company identified a product opportunity: an AI-assisted RFP creation platform they could both use internally and commercialize.
Step 04
What we did
- Designed an application workflow where a buyer (or internal champion) starts by listing core requirements: certifications, integrations, timelines, permissions, compliance needs, marketing requirements, etc.
- Implemented an AI layer that:
  - Ingests these raw requirement lists and departmental inputs.
  - Generates a structured, comprehensive RFP document, organized into logical sections (technical, compliance, functional, commercial, etc.).
- Built collaborative features for multi-department input:
  - Each department can log in and add requirements, comments, and clarifications in their own area.
  - The system merges all inputs into a single RFP draft while preserving ownership and context.
- Implemented RFP versioning and change tracking:
  - Any new or updated requirement creates a new RFP version.
  - All adjustments are recorded with who made them and when.
  - Finalized versions can be exported and sent to vendors.
- Added reusable templates and industry patterns:
  - RFP structures (tags, sections, tasks, departmental roles) from a completed document can be cloned as a starting point for the next RFP.
  - Created industry-specific template baselines (for example, nonprofit vs. construction) that pre-load typical requirements and structure.
Step 05
The new product
- An AI-assisted RFP creation and collaboration platform now offered as a standalone product to the vendor’s customers.
- Primary users: procurement teams, department leads (IT, marketing, compliance, operations), and other stakeholders authoring RFPs.
- Main capabilities:
  - Multi-department requirements capture in a shared workspace.
  - AI-generated, fully structured RFP documents from free-form requirement lists.
  - End-to-end versioning with detailed change history.
  - Template cloning from previous RFPs and industry-specific templates.
  - Exportable, vendor-ready RFP packages.
Step 06
Results & impact
- Buyers can produce complete, structured RFPs significantly faster by aggregating all departmental requirements into one system.
- Reduced RFP chaos (scattered compliance items, overlapping questions, inconsistent sections) via AI-standardized structure.
- Easier iteration: new RFP versions are generated from updated requirements without manual document surgery.
- Substantial time savings for recurring RFPs through reusable templates and industry-specific starting points.
- The tool itself became a new revenue-generating product line, handed off to clients as a differentiating part of the vendor’s offering.
Step 07
Tech highlights
- Web-based, multi-user collaboration environment with role/department-based access.
- AI engine (LLM-based) to transform requirement lists and comments into a coherent, sectioned RFP document.
- Version control and audit trail for requirements, contributors, and RFP document snapshots.
- Template system keyed on tags, departments, and industry types to support rapid reuse and customization.
Step 08
Where we left them
- The software vendor now operates the platform as a standalone product, used both internally and sold to customers for their own RFP workflows.
- They continue to expand template libraries and refine AI prompts to support more industries and complex enterprise requirements.

We use a tight stack that balances speed, robustness, and long‑term maintainability.

Everything You Need to Know

Do we need a fully working tool before we talk?

No. It is enough to have a real internal asset: a heavy spreadsheet, prototype app, script bundle, or R&D tool that people rely on.

What if we only want an internal upgrade, not a product to sell?

That is fine. Many clients start with “make this safe, usable, and maintainable internally.” Productization for external customers can be a later step.

How long does the process take?

Typical ranges: Phase 0 is 4–6 weeks, and Phases 1–3 together usually take about 3–6 months, depending on complexity, integrations, and scope.

How much of our team’s time will this require?

We need access to 1–3 domain experts and a technical contact. Time is heaviest in Phase 0–1 for interviews and reviews, then drops to periodic check‑ins.

Who owns the IP when the project is done?

You do. All code, designs, and documentation specific to your product are yours. We only retain generic, reusable internal tooling and know‑how.

Can you work with our existing tech stack and team?

Yes. Our preferred stack is Django, Next.js, PostgreSQL, Redis, and AWS/GCP, but we can integrate with existing systems and coordinate with your internal engineers.

Is AI mandatory in every project?

No. We only add AI (Gemini, OpenAI, etc.) where it clearly reduces expert effort or user friction. If your data and processes are not ready, we will not force it.

How do we get started?

We start with a short call, and if there is a fit, a fixed‑fee Productization Assessment. You get a clear blueprint and options before committing to a full build.

Selected productization cases

Real products start with real problems

Case: Vendors Q&A

Context & starting point

The internal tool (as we found it)

Why change / trigger

What we did

The new product

Results & impact

Tech highlights

Where we left them

Collaborative vendor Q&A and onboarding platform

Email-native reporting for VFX leadership

Context & starting point

The internal tool (as we found it)

Why change / trigger

What we did

The new product

Results & impact

Tech highlights

Where we left them

Email-native reporting for VFX leadership

AI-assisted RFP creation and collaboration platform

Context & starting point

The internal tool (as we found it)

Why change / trigger

What we did

The new product

Results & impact

Tech highlights

Where we left them

AI-assisted RFP creation and collaboration platform

We use a tight stack that balances speed, robustness, and long‑term maintainability.

Everything You Need to Know

Do we need a fully working tool before we talk?

What if we only want an internal upgrade, not a product to sell?

How long does the process take?

How much of our team’s time will this require?

Who owns the IP when the project is done?

Can you work with our existing tech stack and team?

Is AI mandatory in every project?

How do we get started?