Software in every color.Custom software, with AI that does the work.

Flagship

Havernor

Your whole business, run from one conversation: sales, numbers, operations and meetings in one suite, and nothing important happens until one of your people says yes.

For business owners and their teams

Live

Visit havernor.com(opens in a new tab)

Flagship

DevSpeak

Turns informal requirements into clear technical specs, written for the people who build the software.

For product teams and software developers

Live

Visit www.devspeak.dev(opens in a new tab)

gRit

Turns any code repository into a visual canvas: branches you create, merge and sync by dragging them.

For developers on GitHub and GitLab

Live

Visit gritapp.dev(opens in a new tab)

TalentAssisto

AI assistants trained on your own HR documents: they review resumes, answer policy questions and draft employee documents.

For HR teams and recruiters

Live

Visit www.talentassisto.com(opens in a new tab)

SkipSponsor AI

A free Chrome extension that finds the sponsor read inside a YouTube video and skips it, with one click to undo.

For people who watch YouTube in Chrome

Live

Visit chromewebstore.google.com(opens in a new tab)

DevSpeak Utilities

Four focused tools for the text people hand to AI agents: task specs, safe prompts, clear technical English and prompt testing.

For developers who work with AI agents

Live

Visit utilities.devspeak.dev(opens in a new tab)

Four apps, one suite

• SpecStudioSpecs

  Turns a plain request into a task an AI browser agent can follow and a person can audit.

  Visit specstudio.devspeak.dev(opens in a new tab)
• Prompt GuardianPrompt safety

  Tests system prompts against attacks, and a person approves every change.

  Visit promptguardian.devspeak.dev(opens in a new tab)
• Dev Text ForgeTechnical writing

  Rewrites technical text into clear English and keeps every name and number intact.

  Visit textforge.devspeak.dev(opens in a new tab)
• PromptForgePrompt tools

  A toolbox to write, check, harden and test prompts.

  Visit promptforge.devspeak.dev(opens in a new tab)

See all products →

• 

  Custom business apps

  Internal portals, request and approval flows and document workspaces, built around the way your team already works.

  For exampleA company portal where requests, approvals and documents live in one place. The AI sorts each request and drafts the first answer.

  Talk about this (Custom business apps, opens in a new tab)See details: Custom business apps
• 

  CRM and sales tools

  Your own CRM, shaped to your sales process instead of the other way round.

  For exampleA startup's own CRM. The AI logs every email and call, reminds the team who to follow up with, and drafts the message.

  Talk about this (CRM and sales tools, opens in a new tab)See details: CRM and sales tools
• 

  Machine and operations monitoring

  Dashboards for machines, sensors and operations, with alerts that reach the right person.

  For exampleA manufacturer's telemetry dashboard. The AI watches sensor data, flags a machine that is drifting, and opens a maintenance ticket before it breaks down.

  Talk about this (Machine and operations monitoring, opens in a new tab)See details: Machine and operations monitoring
• 

  Online stores with an AI assistant

  Online stores that answer customers at any hour, in their language.

  For exampleAn online store whose assistant answers product and order questions, day and night, and hands the conversation to staff when a person is needed.

  Talk about this (Online stores with an AI assistant, opens in a new tab)See details: Online stores with an AI assistant
• 

  Process automation

  The copy-and-paste work between email, spreadsheets and your ERP, done for you.

  For exampleInvoices, purchase orders and reports that today get copied by hand. The AI moves them, and a person approves the exceptions.

  Talk about this (Process automation, opens in a new tab)See details: Process automation
• 

  AI consulting and discovery

  Not sure where AI fits in your business? We find out together.

  For exampleWe start with a free 30-minute call, map one process together, and send a custom proposal with scope and timeline.

  Talk about this (AI consulting and discovery, opens in a new tab)See details: AI consulting and discovery

Images: AI-generated illustrations.

1. 01

   A free call

   In 30 minutes you tell us about your business and the problem. We ask questions and tell you honestly whether software and AI can help.

2. 02

   A custom proposal

   You receive a written proposal with scope, timeline and price, fixed before any work starts.

3. 03

   We build, launch and run it

   We build the software, launch it with your team, and keep it running and improving.

Manufacturer

Spot the machine that is drifting before it breaks down

The problem

Machines tend to fail without warning in the middle of a shift. The plant loses production, the team works overtime on emergency repairs, and maintenance follows the calendar instead of the real condition of each machine.

What we would build

For example, a manufacturer could have a dashboard that reads the sensor data its machines already produce, such as temperature, vibration and current. The AI watches the readings day and night, flags a machine whose behaviour is drifting from normal, explains in plain words what changed and drafts a maintenance ticket with the history attached. Under the hood, the agent harness connects to the sensors and the maintenance system, follows your maintenance procedures and is tested on past failures before it goes live.

Your people decide

Your maintenance lead decides whether to stop a machine and approves every work order. The AI never stops a line on its own.

Talk about this use case (opens in a new tab)

What the industry reports

• 35–45%less downtimeU.S. Department of Energy, Federal Energy Management Program
• 70–75%of breakdowns eliminatedU.S. Department of Energy, Federal Energy Management Program
• 5–20%of a plant's productive capacity can be lost to poor maintenanceDeloitte Insights

Show the data as a table

Average results reported after starting a predictive maintenance program

Average results reported after starting a predictive maintenance program	Source
Maintenance costs, lower: 25–30%	U.S. Department of Energy, Federal Energy Management Program
Downtime, lower: 35–45%	U.S. Department of Energy, Federal Energy Management Program
Breakdowns eliminated: 70–75%	U.S. Department of Energy, Federal Energy Management Program
Production, higher: 20–25%	U.S. Department of Energy, Federal Energy Management Program

Ranges from independent surveys compiled by the U.S. Department of Energy (O&M Best Practices Guide, 2010).Source: U.S. Department of Energy, Federal Energy Management Program, “Operations & Maintenance Best Practices Guide, Release 3.0, Chapter 5: Types of Maintenance Programs” (Published 1 Aug 2010; Read on 16 Oct 2026)

Startup

No lead lost to a forgotten follow-up

The problem

A small sales team juggles email, calls and spreadsheets. Notes go missing, follow-ups slip, and sellers spend more time updating records than talking to customers.

What we would build

For example, a startup could have its own CRM where the AI logs every email and call, keeps each deal's history up to date, reminds the team who to contact today and drafts the message for the seller to review. Under the hood, the agent harness reads only your CRM and the team's inbox, follows your sales stages and keeps every outgoing message waiting for a person.

Your people decide

Sellers approve every message before it goes out, and discounts and contract terms always stay with your team.

Talk about this use case (opens in a new tab)

What the industry reports

• 70%of sales reps' time goes to tasks other than sellingSalesforce
• 81%of sales teams are experimenting with AI or have fully implemented itSalesforce

Show the data as a table

Sales teams that saw revenue grow in the past year

Sales teams that saw revenue grow in the past year	Source
Teams with AI: 83%	Salesforce
Teams without AI: 66%	Salesforce

Salesforce State of Sales, 6th edition: survey of 5,500 sales professionals, March to April 2024. A correlation, not proof that AI caused the growth.Source: Salesforce, “Salesforce Report: Sales Teams Using AI 1.3x More Likely to See Revenue Increase (State of Sales, 6th edition)” (Published 25 Jul 2024; Read on 16 Oct 2026)

Online store

Product and order questions answered day and night

The problem

Customers ask the same questions at all hours: is it in stock, where is my order, can I return it. Unanswered questions turn into lost sales, and the team spends its day copying tracking numbers.

What we would build

For example, an online store could add an assistant that answers from the real catalogue, stock and order status, suggests the right size or product, and hands the conversation to a person, with the full context, when the customer needs one. Under the hood, the agent harness connects to your store and shipping systems, answers only from your own policies and is tested on real past conversations before it goes live.

Your people decide

Refunds, exceptions to your policies and changes to an order wait for one of your people.

Talk about this use case (opens in a new tab)

What the industry reports

• 74%of consumers say that, because of AI, they now expect customer service around the clockZendesk
• 80%Forecastof common customer service issues will be resolved by agentic AI without human interventionGartner

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More issues resolved per hour by support agents with an AI assistant

More issues resolved per hour by support agents with an AI assistant	Source
All agents, on average: 14%	NBER Working Paper 31161 / The Quarterly Journal of Economics
New and less experienced agents: 34%	NBER Working Paper 31161 / The Quarterly Journal of Economics

Field study of 5,179 customer support agents, The Quarterly Journal of Economics, 2025. Experienced agents saw minimal impact.Source: NBER Working Paper 31161 / The Quarterly Journal of Economics, “Generative AI at Work (Brynjolfsson, Li and Raymond; The Quarterly Journal of Economics 140(2), 889–942)” (Published May 2025; Read on 16 Oct 2026)

Back office and finance

Invoices and purchase orders that no one has to retype

The problem

Invoices arrive by email as PDFs. Someone retypes them into the ERP, matches them to purchase orders by hand and chases the ones that do not match. It is slow, and every exception holds up the rest.

What we would build

For example, a finance team could have the AI read each invoice, match it to its purchase order and receipt, enter it in the ERP and send a short list of mismatches to the person who can resolve them. Under the hood, the agent harness connects to your mailbox and your ERP, follows your approval rules and checks its work against invoices your team has already processed.

Your people decide

The AI never pays anything. Payments and every exception are approved by your finance team.

Talk about this use case (opens in a new tab)

What the industry reports

• 18.4%of invoices are exceptions that need a person, on averageArdent Partners
• 51.0%of invoices processed straight through, without a person, by best-in-class teamsArdent Partners (report copy distributed by its sponsor, Bottomline)
• 29.0%for all other teamsArdent Partners (report copy distributed by its sponsor, Bottomline)

Show the data as a table

Days to process a single invoice

Days to process a single invoice	Source
Best-in-class teams: 2.9 days	Ardent Partners (report copy distributed by its sponsor, Bottomline)
Average: 8.2 days	Ardent Partners
All other teams: 13.5 days	Ardent Partners (report copy distributed by its sponsor, Bottomline)

Ardent Partners, State of ePayables 2025: survey of 204 AP and finance leaders, March to May 2025.Source: Ardent Partners (report copy distributed by its sponsor, Bottomline), “The State of ePayables 2025: AP's Unfinished Journey (Table 2: The 2025 AP Maturity Framework)” (Published June 2025; Read on 16 Oct 2026) · Source: Ardent Partners, “State of ePayables (Part Nine): AP Benchmarks and Best-in-Class Performance” (Published 22 Jan 2026; Read on 16 Oct 2026)

HR and recruiting

Every application read, and the shortlist prepared

The problem

Open roles bring in hundreds of CVs or, for hard-to-fill roles, too few. Recruiters spend their days reading and scheduling, and good candidates wait or go elsewhere.

What we would build

For example, an HR team could have the AI read each application against the criteria the team wrote, summarise how each candidate meets them with the evidence, answer candidates' questions about the process and propose interview times. Under the hood, the agent harness reads only the role's criteria and the applications, records why it summarised each one the way it did and is checked for consistency before it goes live.

Your people decide

People make every hiring decision. The AI prepares the reading; your recruiters choose who moves forward.

Talk about this use case (opens in a new tab)

What the industry reports

• 69%of HR professionals had difficulty recruiting for full-time rolesSHRM
• 43%of HR professionals say they use AI to support HR work, most of them for recruitingSHRM

Show the data as a table

HR professionals who had difficulty recruiting for full-time roles

HR professionals who had difficulty recruiting for full-time roles	Source
2022: 91%	SHRM
2024: 77%	SHRM
2025: 69%	SHRM

SHRM Talent Trends survey; the 2025 edition surveyed over 2,000 HR professionals.Source: SHRM, “Uncertain Economy Adds to Recruitment Challenges (SHRM 2025 Talent Trends)” (Published 24 Mar 2025; Read on 16 Oct 2026)

Logistics and field service

Routes, stock and visits planned with live information

The problem

Dispatchers plan the day on spreadsheets and phone calls. A late truck or a missing part breaks the plan, customers call to ask where the technician is, and stock piles up in one warehouse while another runs out.

What we would build

For example, a distributor or a field-service company could have an app where the AI proposes the day's routes and visits, re-plans when a delay comes in, warns before a part runs out and keeps each customer informed of the arrival time. Under the hood, the agent harness connects to your orders, inventory and vehicle locations, follows your service rules and is tested on past weeks of real operations.

Your people decide

Dispatchers approve the plan, and purchase orders for stock wait for the person in charge.

Talk about this use case (opens in a new tab)

What the industry reports

• 65%improvement in service levelsMcKinsey & Company

Show the data as a table

Improvement reported by early adopters of AI in supply-chain management

Improvement reported by early adopters of AI in supply-chain management	Source
Logistics costs: 15%	McKinsey & Company
Inventory levels: 35%	McKinsey & Company
Service levels: 65%	McKinsey & Company

McKinsey & Company, 2021: early adopters compared with slower-moving competitors.Source: McKinsey & Company, “Succeeding in the AI supply-chain revolution” (Published April 2021; Read on 16 Oct 2026)

Clinic or health practice

Fewer empty slots in the appointment book

The problem

Patients forget appointments, cancel at the last minute or never call back to reschedule. Every empty slot is time a professional could have spent with another patient, and the front desk spends hours on the phone.

What we would build

For example, a clinic could have an assistant that sends reminders, lets patients confirm, cancel or move their appointment by message, offers the freed slot to the waiting list and keeps the schedule up to date. Under the hood, the agent harness connects only to the scheduling system, never gives medical advice and passes every clinical question to the clinic's staff.

Your people decide

Clinical questions always go to the clinic's staff, and staff confirm any change the clinic's rules do not allow the assistant to make.

Talk about this use case (opens in a new tab)

What the industry reports

• 23%average no-show rate for medical appointmentsHealth Policy (Elsevier), via PubMed
• 67%attended when they received a text notificationBMJ Open, via PubMed Central
• 54%attended without a notificationBMJ Open, via PubMed Central

Show the data as a table

No-show rate with and without electronic text notifications

No-show rate with and without electronic text notifications	Source
Without notifications: 21%	BMJ Open, via PubMed Central
With text notifications: 15%	BMJ Open, via PubMed Central

Meta-analysis of randomised studies, BMJ Open, 2016.Source: BMJ Open, via PubMed Central, “Using digital notifications to improve attendance in clinic: systematic review and meta-analysis (Robotham et al., BMJ Open 6(10), e012116)” (Published 24 Oct 2016; Read on 16 Oct 2026)

Professional services firm

First drafts of proposals, reports and letters in minutes

The problem

Consultants, accountants and agencies spend hours on documents that follow the same structure: proposals, reports, letters to clients. Senior people end up formatting instead of advising.

What we would build

For example, a firm could have a drafting tool that starts from its own templates and past documents, gathers the client's facts, writes the first draft and marks every figure and claim a person must check. Under the hood, the agent harness reads only the firm's approved templates and the client's file, and its drafts are tested against documents the firm has already delivered.

Your people decide

A professional reviews and signs every document. Nothing reaches a client without that review.

Talk about this use case (opens in a new tab)

What the industry reports

• 40%less time on professional writing tasks with a generative AI assistantScience, via PubMed
• 25.1%faster on those tasksHarvard Business School

The same research also found the limit: on a task outside what AI does well, consultants using it were 19% less likely to reach a correct answer. That is why a person reviews every document.

Show the data as a table

Measured effects of AI assistance in controlled experiments

Measured effects of AI assistance in controlled experiments	Source
Less time per writing task: 40%	Science, via PubMed
Higher writing quality: 18%	Science, via PubMed
More consulting tasks completed: 12.2%	Harvard Business School
Faster consulting tasks: 25.1%	Harvard Business School

Noy and Zhang, Science, 2023 (writing tasks); Dell'Acqua et al., Harvard Business School, 2023 (consulting tasks).Source: Science, via PubMed, “Experimental evidence on the productivity effects of generative artificial intelligence (Noy and Zhang, Science 381, 187–192)” (Published 14 Jul 2023; Read on 16 Oct 2026) · Source: Harvard Business School, “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality (HBS Working Paper 24-013)” (Published September 2023; Read on 16 Oct 2026)

Third-party figures from published studies. They describe the industry, not results of DevSpeak Labs projects.

Images: AI-generated illustrations.