What if your Sales team
had an AI teammate who never sleeps?

ProblemMonth-end meant three days of manually matching receipts to bank transactions — error-prone, time-consuming, and dreaded every single time it came around.

SolutionWe built a private bookkeeping automation pipeline that handles the full month-end process without sending any sensitive data to third-party services. Receipts and invoices are fed in, the AI extracts the relevant data, and then matches each transaction against the bank records. Anything the AI isn't confident about gets flagged for human review — so the accountant only touches the edge cases, not the entire pile. A real-time dashboard visualises the state of the books at any point in the month.

ProblemRecordings piled up unprocessed — meetings, voice notes, long YouTube research videos — too long to relisten to, and uploading to third-party transcription tools raised real privacy concerns.

SolutionWe built a chat-based transcription agent that runs entirely on the client's private infrastructure. Drop a voice note, a long audio file, or a YouTube link into the chat — the AI transcribes it, generates a structured summary, pulls out action items, and pushes everything directly into Notion or Google Docs. Nothing touches a third-party transcription service. The client can process a one-hour meeting recording into a clean, structured document in under two minutes.

ProblemContent opportunities across four platforms were missed daily because spotting, writing, designing, and publishing was too slow to do manually — by the time a human got to a trending post, the moment had passed.

SolutionWe built a full content operations platform — a custom dashboard that monitors Twitter, LinkedIn, Instagram, and YouTube for viral posts using engagement and virality scoring. When a content opportunity is spotted, the AI generates three comment variations for LinkedIn engagement. The team can also build branded slide presentations inside the dashboard with a drag-and-drop editor powered by 14 different AI image generation models. One-click publishing sends the approved content directly to Instagram.

ProblemConversion data lived in a spreadsheet no one kept updated. The team had no real-time visibility into which leads were converting or where in the funnel they were dropping off.

SolutionWe built a live conversion tracking dashboard that pulls lead and funnel data directly from a spreadsheet — so the team always has an accurate, up-to-date view without anyone manually updating anything. The dashboard visualises the entire funnel from first touch to close, showing exactly where leads convert and where they drop. We deployed it on the client's existing server infrastructure with zero new services needed, and protected it behind authentication for internal access only.

ProblemCreating one reel meant hours in the editing timeline — clip selection, syncing, B-roll placement — before the creator could publish anything. Output was stuck at one or two reels per week at most.

SolutionWe built an AI Director pipeline for Reelio. The creator uploads raw footage once and describes what they want the reel to say. Our AI scans every clip, detects scene boundaries, tags each one semantically, and removes redundant or unusable footage. Then the AI Director reads the narration script and matches the right clip to each sentence using semantic search. Everything gets synced to the voiceover and assembled into a storyboard the creator can review — swapping any individual clip before the final export. AI voice cloning, autocaptions, and beat sync are all included in the output.

ProblemEvery raw recording needed hours of post-processing — silences trimmed, filler words cut, audio cleaned and levelled — before it was ready to go anywhere. The editing backlog kept growing.

SolutionWe built an automated video post-processing pipeline that handles everything after the record button is pressed. Drop in a raw talking-head recording — the pipeline removes silences and filler words (English and Russian both supported), denoises the audio, applies compression, and normalises the level to broadcast-standard -14 LUFS. A smart hook detector scans the full recording and surfaces the best 5–10 second intro. Then AI generates YouTube chapter titles from the transcript. What used to take hours of manual editing now runs hands-free in a fraction of the time.

ProblemEvery video required manually selecting B-roll clips and timing them over the narration — repetitive, slow, and inconsistent in quality across productions.

SolutionWe built a B-roll automation system that works from a one-time clip library setup. The client supplies their B-roll footage once — we run it through AI transcription to generate word-level timestamps, and AI captions and mood-tags every clip in the library. That index persists permanently. For each new video, an AI Director reads the narration transcript and selects the most contextually relevant clip for each moment, then overlays them with smooth crossfade transitions. New productions reuse the existing library in seconds — no re-processing.